Tools services webmasters counters generators scripts tutorials free
Tools services webmasters counters generators scripts tutorials free
Developed by
Prana Ugiana Gio & Rezzy Eko Caraka
Statistical Calculator (STATCAL)
Easy Statistical Application Program to Educate





Case: Pearson and Spearman Correlation Using
STATCAL, R, SPSS and Jamovi (Part 1)

Part 1: Data & Scatter Plot


The previous article about Pearson correlation.

You Can See This Video (STATCAL, SPSS, Jamovi & R)


1. Data

Suppose given data as follows (Figure 1).


Figure 1 Data


The data above presents height and weight of 10 person. Based on the data above:

=> We will make a scatter plot to know the trend or direction of data distribution.

=> Perform normality test. If normality assumption is satisfied, Pearson correlation will be used. If not, Spearman correlation will be used.

=> Test whether or not significant correlation between height and weight.


2. Scatter Plot

The following are various scatter plot based on STATCAL (R), SPSS and Jamovi.


Figure 2 Scatter Plot Based on STATCAL (R)



Figure 3 Scatter Plot Based on SPSS



Figure 4 Scatter Plot Based on Jamovi



Based on the scatter plot above, we can see the trend of data distribution is getting increase (positive trend). It means that the higher height, the higher weight. We can say that there is a positive relationship between height and weight.



Scatter Plot Based On STATCAL





Scatter Plot Based On SPSS





Scatter Plot Based On Jamovi





#This is R code to create scatter plot

height = c(167.32,163.18,153.17,189.42,174.32,157.36,185.32,167.43,157.42,174.23)

weight = c(69.43,59.17,50.74,90.43,77.43,53.35,80.23,63.35,58.42,67.22)


library(ggplot2)
ggplot(data = NULL, aes(x = height, y = weight)) + geom_point(size = 3) +
   geom_smooth(method=lm)


Scatter Plot Based On R










Pearson and Spearman Correlation with R, SPSS & Minitab

1. Brief Overview of Pearson and Spearman Correlation

Pearson correlation is a value which can be used to measure linear relationship between two variables. Pearson correlation value also notifies dirrection of relationship. Pearson correlation value is lied between -1 and 1. The Pearson linear correlation is also called Pearson product-moment correlation. Notation of Pearson linear correlation for sample is "r", while for population is $$\rho$$ (we read "rho"). If the value of Pearson correlation is so close with -1 or +1, it means that the linear relationship between two variables is so strong.

To use Pearson correlation, the scale of data is continuous or numeric. Michael J. Crawley (2015:108) in his book "Statistics, An Introduction Using R, Second Edition":

"With two continuous variables, x and y, the question naturally arises as to whether their values are correlated with each other (remembering, of course, that correlation does not imply causation). Correlation is defined in terms of the variance of x, the variance of y, and the covariance of x and y (the way the two vary together; the way they co-vary) on the assumption that both variables are normally distributed. We have symbols already for the two variances, $$s_x^2$$ and $$s_y^2$$. We denote the covariance of x and y by cov(x, y), after which the correlation coefficient r is defined as $$r = \frac{cov(x,y)}{\sqrt{s_x^2 s_y^2} }"$$

Spearman correlation is an alternative of Pearson correlation when normality assumption is not satisfied. Peter Dalgaard (2008:123) in his "Introductory Statistics with R, Second Edition":

"As with the one-and two-sample problems, you may be interested in nonparametric variants. These have the advantage of not depending on the normal distribution and, indeed, being invariant to monotone transformations of the coordinates. The main disadvantage is that its interpretation is not quite clear. A popular and simple choice is Spearman's rank correlation coefficient $$\rho$$. This is obtained quite simply by replacing the observations by their rank and computing the correlation. Under the null hypothesis of independence between the two variables, the exact distribution of $$\rho$$ can be calculated."


2. Dataset

The following is R code. You can copy this R code and run in R.


Performance = c(75,65,72,84,74,59,83,55,65,73)

Motivation = c(71,60,78,79,69,55,80,45,78,77)

dframe = data.frame(Performance,Motivation)

dframe

Based on the data above, there two variables, namely performance and motivation.


3. Scatter Plot

The following R code displays scatter plot between motivation and performance.


Performance = c(75,65,72,84,74,59,83,55,65,73)

Motivation = c(71,60,78,79,69,55,80,45,78,77)

dframe = data.frame(Performance,Motivation)

library(ggplot2)
ggplot(data = dframe, aes(x = Motivation, y = Performance)) + geom_point(size = 3) + geom_smooth(method=lm)

Based on the scatter plot above, we can see the trend of data distribution is getting increase (positive trend). It means that the higher motivation, the higher performance. We can say that there is a positive relationship.


4. Pearson Correlation

Now, we will calculate Pearson correlation with R. The following is R code to calculate Pearson correlation.




Performance = c(75,65,72,84,74,59,83,55,65,73)

Motivation = c(71,60,78,79,69,55,80,45,78,77)

cor.test(Motivation, Performance)

Based on the result above, we obtain Pearson correlation is 0.8239. Note that the value of Pearson correlation is positive. It means that there is postive relationship between motivation and performance. The higher motivation, the higher performance. Beside that, the p-value is 0.003377 < level of significance 0.05, so we can conclude that there is significant relationship between motivation and performance.

The following are SPSS and Minitab result for Pearson correlation which give same result with R.


Table 1 SPSS Result for Pearson Correlation


Table 2 Minitab Result for Pearson Correlation

Based on SPSS result in Table 1, the value of Pearson correlation is 0,824, with p-value is 0,003. While based on Minitab result in Table 2, the value of Pearson correlation is 0,824, with p-value 0,003, which give same result with R.


5. Size an Effect

Andy Field (2009:170) in his book "Discovering Statistics Using SPSS, Third Edition":

"We also saw in section 2.6.4 that because the correlation coefficient is a standardized measure of an observed effect, it is a commonly used measure of the size of an effect and that values of $$\pm{0.1}$$ represent a small effect, $$\pm{0.3}$$ is a medium effect and $$\pm{0.5}$$ is a large effect (although I re-emphasize my caveat that these canned effect sizes are no substitute for interpreting the effect size within the context of the research literature)."

Based on explanation above: $$Value \ of \ Pearson \ correlation \ is \ \pm{0.1} \ represent \ a \ small \ effect.$$ $$ Value \ of \ Pearson \ correlation \ is \ \pm{0.3} \ represent \ a \ medium \ effect.$$ $$ Value \ of \ Pearson \ correlation \ is \ \pm{0.5} \ represent \ a \ large \ effect.$$

Based on the preceding explanation, correlation between motivation and performance is 0,824 which is greater than 0,5. It means that motivation and performance is strong correlated.



6. Normality Assumption

One of assumption in Pearson correlation is normality assumption, namely both samples are assumed from normal distribution populations. Andy Field (2009:178) in his book "Discovering Statistics Using SPSS, Third Edition":

"However, if you want to establish whether the correlation coefficient is significant, then more assumptions are required: for the test statistic to be valid the sampling distribution has to be normally distributed and as we saw in Chapter 5 we assume that it is if our sample data are normally distributed (or if we have a large sample). Although typically, to assume that the sampling distribution is normal, we would want both variables to be normally distributed. "

Based on explanation above, When normality assumption shoud be tested?

The answer is when we will do the significant test of Pearson correlation.

Why?

It is because, fulfilled or not of normality assumption will affect the sampling distribution of Pearson correlation statistics. When normality assumption is satisfied, so the sampling distribution of Pearson correlation statistic will form t distribution, so that the rule of t distribution can be used. When normality assumption is not satisfied, so the sampling distribution of Pearson correlation statistic will so far from t distribution, so that the conclucion become not accurate (misleading conclucion).

The following is R code to test normality assumption using Komogorov-Smirnov test.


Performance = c(75,65,72,84,74,59,83,55,65,73)

Motivation = c(71,60,78,79,69,55,80,45,78,77)


ks.test(Motivation,"pnorm", mean(Motivation), sd(Motivation), exact = FALSE  )

ks.test(Performance,"pnorm", mean(Performance), sd(Performance), exact = FALSE  )

Based on Kolmogorov-Smirnov test above, we obtain p-value of motivation is 0.604 > level of significance 0.05. So we conclude that normality assumption (population) for motivation data is satisfied.

We also obtain p-value of performance is 0.9539 > level of significance 0.05. So we conclude that normality assumption (population) for performance data is satisfied.

the following is Kolmogorov-Smirnov result for normality test based on SPSS.


Table 3 SPSS Result for Kolmogorov-Smirnov Test

Based on the result of Kolmogorov-Smirnov test above, we obtain p-value for motivation is 0,954 and p-value for motivation is 0,604. Both p-value > significant level 0,05. It means that normality assumption (population) for motivation and performance data are satisfied.


7. Spearman Correlation

Spearman correlation is an alternative for Pearson correlation when normality assumption is not satisfied. The following is R code for performing Spearman correlation.


Performance = c(75,65,72,84,74,59,83,55,65,73)

Motivation = c(71,60,78,79,69,55,80,45,78,77)


cor.test(Motivation,Performance, method = c("spearman"))

Based on the result above, we obtain Spearman correlation is 0.7408537. Note that the value of Spearman correlation is positive. It means that there is postive relationship between motivation and performance. The higher motivation, the higher performance. Beside that, the p-value is 0.01423 < level of significance 0.05, so we can conclude that there is significant relationship between motivation and performance.

The following is Spearman correlation result based on SPSS.

Table 4 SPSS Result for Spearman Test

Based on SPSS result for Spearman correlation above, we obtain Spearman correlation is 0,741 with p-value is 0,014 which give same result with R.



Paired-Samples T Test and Wilcoxon Test with R, SPSS and Minitab

1. Brief Overview of Paired-Samples t Test

Paired-samples t test (dependent t test) and Wilcoxon test can be used to test whether or not significant difference (statistically) based on 2 related-samples (2 paired-samples).


2. Data

The following is R code that show our data.


#COPY THIS R CODE AND RUN IN R TO SEE THE DATA

weight_before = c(85,79,83,77,85,78)

weight_after = c(84,74,80,76,83,77)


dframe = data.frame(weight_before, weight_after)
colnames(dframe) = c("weight before consuming diet medicine","weight after consuming diet medicine")
dframe

print("Average of weight before consuming diet medicine:")
mean(weight_before) #calculate mean
print("Average of weight after consuming diet medicine:")
mean(weight_after) #calculate mean


Figure 1 Our Data



Based on the data above is presented weight of 6 persons, before and after consuming diet medicine XYZ in a week. The average of weight before consuming diet medicine XYZ is 81,16667, while the average of weight after consuming diet medicine XYZ is 79. On average, there is a reduction of weight after consuming diet medicine XYZ in a week.

In this case, we will use paired-samples t test to test whether or not significant difference in weight before and after consuming diet medicine in a week.



3. Paired-Samples t Test Using R Language

The following is R code to perform paired-samples t test.


#COPY THIS R CODE AND RUN IN R TO PERFORM PAIRED-SAMPLES T TEST


weight_before = c(85,79,83,77,85,78)

weight_after = c(84,74,80,76,83,77)

t.test(weight_before,weight_after,paired=TRUE)



Figure 2 Result of Paired-Samples t Test Using R


Based on result of paired-samples t test above, we obtain p-value 0,02118 < level of significance 0,05, so we can conclude that there is significant difference in weight before and after consuming diet medicine in a week.

The following is result of paired-samples t test using SPSS and Minitab, which give the same result with R.


Figure 3 Paired-Samples t Test Using SPSS

SPSS


Figure 4 Paired-Samples t Test Using Minitab

Minitab


Based on SPSS result in Figure 3, we obtain the p-value is 0,021177, while p-value in Minitab (Figure 4) is 0,021. Both p-value are respectively same with p-value of R.


4. Normality Assumption

One of assumption in paired-samples t test is normality assumption, namely population of difference of paired-data is assumed to be normally distributed.

Andy Field (2009:329) in his book "Discovering Statistics Using SPSS, 3rd Edition":

"9.4.3. The dependent t-test and the assumption of normality
We talked about the assumption of normality in Chapter 5 and discovered that parametric tests (like the dependent t-test) assume that the sampling distribution is normal. This should be true in large samples, but in small samples people often check the normality of their data because if the data themselves are normal then the sampling distribution is likley to be also. With the dependent t-test we analyse the differences between scores because we’re interested in the sampling distribution of these differences (not the raw data). Therefore, if you want to test for normality before a dependent t-test then what you should do is compute the differences between scores, and then check if this new variable is normally distributed (or use a big sample and not worry about normality!). It is possible to have two measures that are highly non-normal that produce beautifully distributed differences!"

The following is R code to test normality assumption. I use Kolmogorov-Smirnov test to test normality assumption.



#THIS IS R CODE TO TEST NORMALITY ASSUMPTION USING DIFFERENCE OF PAIRED-DATA


weight_before = c(85,79,83,77,85,78)

weight_after = c(84,74,80,76,83,77)


difference = weight_after - weight_before


dframe = data.frame(weight_before, weight_after, difference)
colnames(dframe) = c("weight before consuming diet medicine","weight after consuming diet medicine","difference")

dframe


ks.test(difference,"pnorm", mean(difference), sd(difference), exact = FALSE  )



Figure 5 Testing of Normality Assumption using Kolmogorov-Smirnov Test Based on R Language


Based on the result above (Figure 5), we obtain p-value 0,7876 > level of significance 0,05, so we can conclude that normality assumption of difference paired-data is satisfied.

The following is SPSS result to perform normality test using Kolmogorov-Smirnov test.


Figure 6 Testing of Normality Assumption using Kolmogorov-Smirnov Test Based on SPSS


Based on SPSS result above, we obtain p-value (Asymp. Sig. (2-tailed)) 0,787 > level of significance 0,05, so we can conclude that normality assumption of difference paired-data is satisfied.


5. Normality Assumption Can Be Ignored

In paired-samples t test, normality assumption can be ignored. Andy Field (2009:329) in his book "Discovering Statistics Using SPSS, 3rd Edition":

"9.4.3. The dependent t-test and the assumption of normality
We talked about the assumption of normality in Chapter 5 and discovered that parametric tests (like the dependent t-test) assume that the sampling distribution is normal. This should be true in large samples..."

Based on explanation above, if size of sample is large, normality assumption can be ignored, because the sampling distribution is normally distributed.

When the sample size is considered to be large?

Murray R. Spiegel dand Larry J. Stephens (2008:275-276) in their book "Statistics 4th Edition":

"In previous chapters we often made use of the fact that for samples of size N > 30, called large samples, the sampling distributions of many statistics are approximately normal, the approximation becoming better with increasing N."

Based on the explanation above, sample size is considered to be large if > 30.


6. Wilcoxon Test

Wilcoxon test is an alternative of paired-samples t test. When sample size is < 30 and normality assumption is not satisfied, Wilcoxon test can be an alternative for paired-samples t test. The following is R code to perform Wilcoxon test.



#THIS IS R CODE TO PERFORM WILCOXON TEST

weight_before = c(85,79,83,77,85,78)

weight_after = c(84,74,80,76,83,77)

wilcox.test(weight_after, weight_before, paired = TRUE, correct = FALSE)


Figure 7 Result of Wilcoxon Test Using R


Based on result of Wilcoxon test above, we obtain p-value 0,02601 < level of significance 0,05, so we can conclude that there is significant difference in weight before and after consuming diet medicine in a week. The following is result of Wilcoxon test test using SPSS, which give the same result with R.


Figure 8 Result of Wilcoxon Test Using SPSS


Based on result of Wilcoxon test above, we obtain p-value is 0,02601 < level of significance 0,05, so we can conclude that there is significant difference in weight before and after consuming diet medicine in a week.

Repeated Measures ANOVA and Friedman Test with STATCAL and SPSS

In this page, you can download our article "Repeated-Measures ANOVA and Friedman Test with STATCAL and SPSS".



Repeated-measures ANOVA and Friedman Test using SPSS



Repeated-measures ANOVA and Friedman Test using STATCAL















6 Steps Run Your Shiny Apps with Shortcut

In this article, i will explain, step by step how to make your shiny apps can be run with a shortcut (without run RStudio). I formulate 6 steps to do it. Here, i assume you have had shiny apps.

You Can See This Video (Bottom)

1. Create a Folder for Working

The first step is creating a folder for working. Here, i make a folder with name "MYAPPS". The location of this folder at C:.


Figure 1 Create a Folder for Working at C:


2. Save Your UI and SERVER File in MYAPPS Folder

The second step, i save ui.r and server.r file in MYAPPS folder. Figure 3 and Figure 4 are respectively ui.r and server.r file.


Figure 2 Save ui.r and server.r File at MYAPPS Folder


Figure 3 ui.r File


Figure 4 server.r File


3. Create run.r File

The third step is creating run.r file (Figure 5).


Figure 5 Create run.r File


4. Get Location of R.exe

The fourth step is get location of R.exe (Figure 6).

Figure 6 Get Location of R.exe


Based on Figure 6, we obtain location of R.exe at C:\Program Files\R\R-3.4.3\bin.


5. Create MYAPPS.bat File

The fifth step is making a MYAPPS.bat file (Figure 7).


Figure 7 Create MYAPPS.bat File




6. Create Shortcut and Run Your Shiny Application

The last step is creating a shortcut and run shiny application with this shortcut (Figure 8).


Figure 8 Create Shortcut and Run Your Shiny Application



You Can See This Video (Bottom)
This Video Explains Step by Step to Make Shiny Apps Can be Launched with a Shortcut



Kuliah Online Bahasa Pemrograman R
Pertemuan: Ke-I
Materi: Mendownload dan Menginstal R, RStudio, Paket R

Kuliah Online Bahasa Pemrograman R
Pertemuan: Ke-II
Materi: Vektor dalam R

Kuliah Online Bahasa Pemrograman R
Pertemuan: Ke-III
Materi: Fungsi Dasar R

Kuliah Online Bahasa Pemrograman R
Pertemuan: Ke-IV
Materi: Operator Pembanding & Operator Logika

Kuliah Online Bahasa Pemrograman R
Pertemuan: Ke-V
Materi: List dalam R

Kuliah Online Bahasa Pemrograman R
Pertemuan: Ke-VI
Materi: Data Frame dalam R

Kuliah Online Bahasa Pemrograman R
Pertemuan: Ke-VII
Materi: Factor dalam R

Kuliah Online Bahasa Pemrograman R
Pertemuan: Ke-VIII
Materi: Struktur Pengulangan (for dan while) dan Kondisi (if) dalam R

Kuliah Online Bahasa Pemrograman R
Pertemuan: Ke-IX
Materi: Latihan Membuat Fungsi dalam R (Fungsi Menghitung Luas Persegi, Luas Persegi Panjang dan Luas Lingkaran)

Kuliah Online Bahasa Pemrograman R
Pertemuan: Ke-X
Materi: Latihan Membuat Fungsi dalam R (Menentukan Apakah Suatu Bilangan Buat Termasuk ke Dalam Bilangan Bulat Positif atau Negatif; Genap atau Ganjil; Netral)

Kuliah Online Bahasa Pemrograman R
Pertemuan: Ke-XI
Materi:
1. Cara Menginstal Paket R Shiny
2. Membuat Aplikasi R Shiny Sederhana
3. Menjalankan Aplikasi R Shiny dengan Shortcut (Tanpa Menjalankan RStudio)
4. Mempublish Aplikasi R Shiny Sehingga Dapat Digunakan Secara Online

Arah Sebaran Data

Mengukur Keeratan Hubungan Linear dengan Korelasi Linear Pearson

Menghitung Nilai Korelasi Linear Pearson dengan STATCAL

Menghitung Nilai Korelasi Linear Pearson dengan SPSS

Membuat Grafik Sebaran Data dengan STATCAL

Partial Least Squares Structural Equation Modeling dengan Software WarpPLS, Contoh 1, Pengujian Mediasi

Partial Least Squares Structural Equation Modeling PLS SEM dengan Software SmartPLS, First Order

Partial Least Squares Structural Equation Modeling PLS SEM dengan Software SmartPLS, Second Order

CONFIRMATORY FACTOR ANALYSIS DENGAN LISREL DAN STATCAL, CONTOH 1

CONFIRMATORY FACTOR ANALYSIS SECOND ORDER DENGAN LISREL DAN STATCAL, CONTOH 1

Confirmatory Factor Analysis (CFA) 1 Variabel Laten

Regresi Linear Berganda




Regresi Data Panel




PATH ANALYSIS DENGAN LISREL DAN STATCAL, CONTOH 1




Part 1: Contoh Data




Part 2: Mengimport Data ke EViews




Part 3: Menginput Data ke STATCAL




Part 4: Statistik Deskriptif dengan EViews dan STATCAL




Part 5: Uji Stasioner pada Level




Part 6: Uji Stasioner pada First Difference




Part 7: Uji Kointegrasi




Part 8: Persamaan Jangka Panjang




Part 9: Persamaan Jangka Pendek








































kata kunci: belajar spss pdf, belajar spss 22, belajar spss 16, belajar spss untuk pemula, belajar spss 23, belajar spss 25, belajar spss 24, belajar spss dasar, belajar spss uji validitas, belajar spss untuk skripsi, belajar spss anova, belajar aplikasi spss, belajar spss bagi pemula, belajar spss buat skripsi, belajar otodidak spss pasti bisa, buku belajar spss, belajar cepat spss, belajar spss dengan cepat, cara belajar spss bagi pemula, cara belajar spss 16, belajar spss dasar pdf, belajar spss dengan mudah, tutorial belajar spss dasar, belajar statistik dengan spss, belajar input data spss, belajar mengolah data spss, ebook belajar spss, belajar spss indonesia, belajar ibm spss, jasa belajar spss, modul belajar spss lengkap, download modul belajar spss lengkap, belajar spss mudah, belajar statistik menggunakan spss, modul belajar spss, modul belajar spss 18, belajar otodidak spss, belajar spss pemula, belajar program spss, belajar spss 20 pdf, belajar spss 16 pdf, belajar spss regresi, belajar tentang spss, tutorial belajar spss pdf, tujuan belajar spss, belajar spss video, belajar spss 17, belajar spss 18, belajar spss 15, belajar spss 20, belajar spss 20 untuk pemula statistika adalah, statistika deskriptif, statistika matematika, statistika kelas 12, statistika inferensial, statistika dasar, statistika pendidikan, statistika anova, statistika akuntansi, statistika adalah pdf, statistika analisis regresi, statistika angka indeks, statistika aktuaria, statistika anova pdf, statistika adalah kegiatan untuk, statistika a informatika, tabel a statistika, akreditasi a statistika, italia serie a statistika, excel a statistika, statistika bisnis 2, statistika brawijaya, statistika buku, statistika bisnis pdf, statistika binomial, statistika belajar apa, statistika brainly, statistika bsi, statistika berdasarkan metodenya, statistika contoh soal, statistika chi square, statistika chi kuadrat, statistika coding, statistika contohnya, statistika cukup, statistika contoh soal diagram lingkaran, statistika cr, statistika contoh populasi dan sampel, statistika cara mencari modus, statistika c.ronaldo, c dalam statistika, cara mencari x statistika, statistika, hepatitis c statistika, statistika dan probabilitas, statistika dasar pdf, statistika dan statistik, statistika deskriptif pdf, d(x) statistika, cohenovo d statistika, statistika ekonomi dan bisnis, statistika ekonomi pdf, statistika ekonomi dan bisnis pdf, statistika ekonomi ut, statistika ekonomi adalah, statistika excel, statistika ekonomi 1, statistika frekuensi relatif, statistika farmasi pdf, statistika f(x), tabel f statistika regresi linear sederhana regresi linear adalah regresi linear berganda adalah regresi linear sederhana adalah regresi linear berganda spss regresi linear berganda pdf regresi linear berganda manual regresi linear spss regresi linear adalah pdf regresi linier anova regresi linear aplikasi regresi linier adalah pdf regresi non linear adalah uji regresi linear adalah persamaan regresi linear adalah regresi linear berganda 3 variabel regresi linear berganda 3 variabel bebas regresi linear contoh regresi linear calculator regresi linear casio regresi linear cara spss regresi linier calculator regresi linear sederhana contoh soal regresi linear berganda contoh regresi linear berganda dan contoh soal regresi linear dengan excel regresi linear dan korelasi regresi linear dengan spss regresi linear dengan variabel dummy regresi linier dan nonlinear regresi linear data mining regresi linear di spss regresi linear dengan kalkulator regresi linear excel 2016 regresi linear excel 2013 regresi linier excel 2010 regresi linier eksponensial dan polinomial regresi linier eksponensial regresi linear equation regresi linear sederhana excel analiza e regresi linear regresi linear fisika regresi linear formula regresi linier forecasting regresi linier forex metode regresi linear forecasting fungsi regresi linear fungsi regresi linear berganda formula regresi linear berganda uji f regresi linear sederhana uji f regresi linear berganda uji f regresi linear uji f pada regresi linear sederhana regresi linear ganda regresi linear ganda spss regresi linier ganda pdf regresi linier ganda spss regresi linier ganda manual regresi linier glbb makalah regresi linear ganda pengertian regresi linear ganda regresi linier hirarki hasil regresi linear berganda hipotesis regresi linear berganda hasil regresi linear berganda negatif hipotesis regresi linear sederhana hipotesis regresi linear hasil regresi linear sederhana hitung regresi linear hasil regresi linear sederhana negatif hasil regresi linear interpretasi regresi linear berganda interpretasi regresi linear interprestasi regresi linear berganda interpretasi regresi linear sederhana regresi linier jurnal regresi linear jurnal regresi linear berganda jurnal jenis regresi linear jurnal regresi linear pdf jurnal regresi linier sederhana pdf regresi linear kalkulator regresi linear kuadratik regresi linear kimia regresi linier kalkulator casio regresi linier kuadrat terkecil regresi linier kuadratik regresi linier korelasi regresi linier kuadrat regresi linier komputasi numerik regresi linier logistik regresi log linier perbedaan regresi linear dan logistik langkah regresi linear berganda regresi linear non linear regresi linear matlab regresi linear manual regresi linear multivariat regresi linear multiple korelasi adalah korelasi artinya korelasi product moment korelasi pearson korelasi spearman korelasional korelasi dan regresi korelasi parsial korelasi rank spearman korelasi antara korelasi agama dan manusia korelasi antara organisasi dan manajemen bisnis korelasi antara kerja keras dan tanggung jawab korelasi antar dimensi korelasi antar masyarakat korelasi atau hubungan antar tulang yang pada pergerakannya bersifat terbatas adalah korelasi antara tingginya curah hujan dan kekeringan bernilai korelasi bivariat korelasi biserial korelasi berganda adalah korelasi berat tubuh dan frekuensi denyut jantung korelasi berganda spss korelasi berganda pdf korelasi batuan korelasi biserial pdf korelasi bernilai negatif korelasi contoh soal korelasi cbr dan daya dukung tanah korelasi contoh korelasi citra korelasi cramer korelasi cbr dengan qc korelasi c++ korelasi csr dengan sustainability development korelasi chi square korelasi chi square spss korelasi dalam statistik korelasi dan regresi adalah korelasi ddt dan cbr korelasi determinasi korelasi data kualitatif korelasi dan regresi ppt korelasi dan regresi sederhana korelasi dengan spss korelasi excel korelasi etika korelasi eta korelasi etika dan pancasila korelasi ekonomi pembangunan terhadap pembangunan ekonomi korelasi etika profesi korelasi ekonomi dan politik terhadap proses pembangunan bangsa korelasi etika profesi teknologi dan hukum terhadap informasi korelasi eta pdf korelasi emas dan dolar korelasi forex korelasi filsafat manusia dengan disiplin ilmu psikologi korelasi filsafat hukum dan keadilan korelasi filsafat dengan kebudayaan korelasi film sang kiai korelasi fisik kreatif dan rasio korelasi filsafat dakwah dan filsafat barat korelasi fatwa dengan ijtihad korelasi fitrah dengan pengembangan kepribadian islam korelasi fiqih dan ushul fiqh f tabel korelasi ganda korelasi genetik korelasi ganda pdf korelasi ganda spss korelasi genetik adalah korelasi genetik pdf korelasi geologi korelasi gamma korelasi geografi korelasi ganda dengan excel korelasi hukum korelasi hubungan korelasi hubungan antar variabel korelasi hukum dan ham korelasi ham korelasi hierarchy organizations dengan type of plans korelasi harga emas dengan dollar korelasi hak dan kewajiban korelasi hukum internasional dan power korelasi hubungan ptk korelasi itu apa korelasi iman dan taqwa korelasi iman islam dan ihsan korelasi islam korelasi iman ilmu dan amal korelasi islam dan sains korelasi index dengan forex korelasi iman korelasi islam dan ilmu pengetahuan korelasi jabatan korelasi jurnalistik dan kehumasan korelasi jurnal korelasi jamak korelasi jaspen korelasi judul skripsi korelasi jonathan sarwono transformasi data negatif transformasi data eviews transformasi data ordinal ke interval transformasi data pdf transformasi data menjadi informasi transformasi data mentah menjadi suatu bentuk transformasi data persentase transformasi data spss transformasi data adalah transformasi data autokorelasi transformasi data agar homogen transformasi data angket cara transformasi data arcsin cara transformasi data akar kuadrat cara transformasi data agar normal dengan spss transformasi data bernilai negatif transformasi data berdasarkan histogram transformasi data bernilai nol transformasi data box cox transformasi basis data transformasi data agar berdistribusi normal cara transformasi data bernilai negatif contoh transformasi basis data transformasi data fisik ke bahasa sql transformasi data kedalam bentuk log transformasi data dengan cara sqrt transformasi data dengan cara ln cara transformasi data ke logaritma natural eviews cara transformasi data dengan excel cara transformasi data negatif di spss cara transformasi data ordinal ke interval dengan spss cara transformasi data dengan spss transformasi data dengan sqrt transformasi data dengan r transformasi data dengan log transformasi data dalam data mining transformasi data dengan msi transformasi data dengan stata transformasi data excel transformasi data menggunakan excel transformasi data tidak normal dengan eviews proses transformasi data dari excel ke mapinfo transformasi data first difference transformasi data first difference spss fungsi transformasi data transformasi erd ke basis data fisik transformasi data ghozali gambaran transformasi data menjadi informasi gambarkan transformasi data menjadi informasi transformasi data heteroskedastisitas transformasi data tidak homogen transformasi data tidak homogen spss transformasi data uji heteroskedastisitas transformasi data tidak homogen dengan spss cara transformasi data tidak homogen di spss cara transformasi data tidak homogen spss transformasi data inverse transformasi data interval ke rasio cara transformasi data inverse jenis transformasi data jurnal transformasi data jenis transformasi data pdf jelaskan transformasi data menjadi informasi jenis transformasi data record transformasi data kuadrat transformasi data ke dalam informasi transformasi data kuantitatif menjadi kualitatif transformasi data ke ln transformasi data kuesioner transformasi data keuangan transformasi data kualitatif transformasi data logaritma transformasi data lag transformasi data ln transformasi data log spss transformasi data log transformasi data lg10 tujuan transformasi data log transformasi data spss dengan ln transformasi data minitab transformasi data msi transformasi data menggunakan spss transformasi data menjadi normal transformasi data normal transformasi normalitas data spss transformasi data tidak normal transformasi data tidak normal dengan excel transformasi data ordinal ke interval dengan excel transformasi data ordinal ke interval spss transformasi data outlier transformasi data ordinal ke nominal cara transformasi data ordinal menjadi interval software transformasi data ordinal ke interval langkah-langkah transformasi data ordinal ke interval transformasi data pada spss transformasi data panel transformasi data pada data mining transformasi data pls transformasi data pada minitab transformasi data primer regresi adalah regresi linier berganda regresif regresi berganda regresi linier sederhana regresi logistik regresi linear berganda regresi sederhana regresi linear sederhana regresi artinya regresi adalah pdf regresi analisis regresi anova regresi auxiliary regresi atas variabel dummy regresi air laut adalah regresi arima regresi adalah psikologi a regresión linear regresiones a vidas pasadas regresion a vidas pasadas regresion a la media regresiones a vidas pasadas testimonios regresión a vidas pasadas regresion a vidas pasadas brian weiss regresiones a vidas pasadas gratis regresiones a vidas pasadas como hacerlo regresion a la infancia regresi berganda pdf regresi berganda di excel regresi berganda manual regresi berganda dengan matriks regresi binomial negatif regresi berganda dengan variabel kontrol regresi berganda eviews b regresion lineal rumus b regresi koefisien regresi b bertanda negatif kurva regresi b hcg exp b regresion logistica coeficiente b regresion lineal interpretacion exp(b) regresion logistica formula de b regresion lineal y=mx+b regresion lineal a y b regresion lineal regresi cox regresi cross section regresi contoh regresi cox proportional hazard adalah regresi canonical regresi cox proportional hazard regresi cox dengan spss regresi cross sectional adalah regresi cox pdf regresi canonical adalah linear regression c code cuenta regresiva c sharp regresi dan korelasi regresi data panel dengan spss regresi dan korelasi sederhana regresi data mining regresi dengan spss regresi data panel pdf regresi di excel regresi dan korelasi adalah d. coeficiente de regresión d regressiva sinonimo de regresión antonimo de regresion significado de regresivo antonimo de regresivo recta de regresion regresi eksponensial regresi excel regresi eviews regresi ekonometrika regresi ecm regresi excel 2016 regresi excel grafik regresi ekonomi regresi eksponensial pdf regresi eksponensial excel e dalam regresi linier berganda regresion e interpolacion regresion de hipnosis regresiones e hipnosis analiza e regresionit padia e regresit ce e regresia vija e regresionit regresi filial regresi fungsional regresi fungsional adalah regresi fixed effect model regresi fungsi upah regresi fourier adalah regresi fuzzy regresi forecasting regresi faktor uji t adalah uji t test uji tarik uji t berpasangan uji t spss uji t independen uji toksisitas uji teori sim c uji t dan uji f uji t adalah pdf uji t aksen uji t anova uji t artinya uji t atau uji parsial uji t analisis regresi sederhana uji t aksen dengan spss uji t ada berapa macam uji t analisis regresi berganda cara uji t dengan spss pengertian uji t cara uji t pengertian uji t menurut para ahli pengertian uji tarik cara uji torniquet pengertian uji t menurut sugiyono pengertian uji toksisitas uji t berpasangan dengan spss uji t bebas uji t bab 3 uji t berpasangan dan tidak berpasangan uji t beda uji t bebas adalah uji t berpasangan manual uji t contoh soal uji t cara uji t contoh uji t cara manual uji t cara membaca uji t cuplikan kembar uji t cara spss uji t.com uji t dan chi square uji t berpasangan contoh soal contoh soal uji teori sim c cara uji tes sim c uji t dengan spss uji t dua sampel berpasangan uji t dependen pdf uji t dependen dan independen diuji terus diuji terus menerus d tabel uji normalitas uji di terminal biar diuji takkan ku berhenti biar di uji takkan tabel d uji kolmogorov smirnov tabel d uji binomial tabel d uji tanda diuji uji t excel uji t eviews uji t eksperimen uji t ekonometrika uji t kelas eksperimen dan kontrol uji t satu ekor interpretasi uji t eviews uji t statistik excel e tilang uji coba uji tilang e cctv uji traduccion e interpretacion 6 janar dita e ujit te bekuar uji traduccion e interpretacion nota corte fabrika e uji tepelena uji t f z uji t fisher uji t f spss uji t dan f dengan eviews uji t dan f spss uji t dan f dengan spss uji t uji f koefisien determinasi rumus uji t fisher uji t uji f pdf uji t dan f menurut para ahli uji f tabel f tabel uji homogenitas uji f dan t uji t ghozali 2016 uji t ghozali 2013 uji t ghozali 2011 uji t ghozali uji t gain score uji t ghozali 2006 uji t grafik uji t menurut ghozali 2016 uji t menurut gujarati uji t one group design uji t hitung uji t hipotesis uji t hotelling uji t hitung dengan spss belajar sem lisrel belajar sem amos belajar sem dengan lisrel belajar sem dengan amos belajar seo sem cara belajar sem anova adalah anova satu arah anova dua arah anova spss anova pdf anova dua jalur anova satu jalur anova test anova two way anova adalah pdf anova analysis anova adalah uji anova analisis anova adalah jurnal anova analysis of variance pdf anova ancova anova anova anova ancova manova a anova estatistica an anova test an anova sous vide an anova table anova a b a oneway anova the anova from a randomized complete block experiment output is shown below anova a due vie anova a una via anova a misure ripetute anova bali anova berikan contohnya anova block design anova bonferroni test anova book anova band anova bivariate anova bandung anova bag anova by excel anova b anova b spss anova calculator anova culinary anova contoh soal anova chi square anova cooking anova contoh anova crd anova culinary recipe anova curve anova communications ss&c anova anova dengan spss anova dua arah dengan spss anova dua arah ppt anova digunakan untuk anova di excel dianova dianova rabbani dianova instagram dianova laboratories dianova antibodies dianova diabetes centre dianova lapstone dianova gmbh dianova canada dianova scientology anova excel 2013 anova excel 2010 anova example anova excel 2016 anova equation anova effect size anova example problem anova excel add in anova excel how to use e anova test anova e t-student anova e tukey anova e teste t anova e ancova anova e t-student como funcionam anova e tukey no excel anova e kruskal wallis anova e regressione lineare anova r post hoc anova food anova faktorial anova food usa anova food bali anova food llc anova food indonesia anova formula anova faktorial spss anova factorial anova for regression f anova spss f tabel anova f anova meaning f anova calculator f anova significado