Question

Workbook 2.9. Use SPSS or your choice to create a data file with two variables based on a data set with 40 participants. Your two variables should be continuous measures ranging from o to 50. Again, try to use the whole range of values for both variables but try to enter the data so that they produce a positive relationship thigh score on Variable 1 associated with high scores on Variable 2) reflecting a positive correlation. In the end, if I used temperature in Celsius as variable and ice cream sales in a day) at the mall's Dairy Queen (in dollars), my data file would look something like this 4:33 PM example, if my groups reflected different treatment groups and my dependent variable was the score on the Mental Rotation Test (MRT), in "Data View my data file would look like this: Showing value labels: Group MRT Control Control Treatment 15 Placeho Placebo ete (15 in group) For your workbook: Use R (or RStudio) to compute an analysis of variance (the group variable is the independent variable and the score variable is the dependent variable). Print the results and include them in your workbook. If you did the data entry correctly, the ANOVA results should he significant with p<.05. If not, fix the data for lose marks). 4:33 PM el (40 in total) For your workbook: Use SPSS or to compute a Pearson correlation and a regression analysis (which variable is the predictor and which one is the dependent variable is your choice). Print the results and include them in your workbook. If you did the data entry correctly, the correlation and regression results should be positive and significant with p<05. If not, fix the data for lose 4:33 PM

for this workbook u can make up your own data. it doesnt matter

Answer 1

Both the parts are done in R

Code along with output is attached herewith.

######PART 1##*********** # Data Generation dfl <- data.frame (Group = rbinom (45, 2, 0.5), MRT = rpois ( 45, 45)) df1$Group[df1 $Group == 0] = "Control" dfi$Group[df 1 $Group == 1] = "Treatment" dfi$Group[df 1$Group == 2] = "Placebo" df1$Group <- factor(df1$Group) # Data head(dfl) Group MRT ## i Control 50 ## 2 Control 47 ## 3 Control 36 ## 4 Treatment 48 ## 5 Treatment 51 ## 6 Treatment 43 # Model fitting ml <- aov (MRT-Group, data = dfl) summary (ml) ## ## Group ## Residuals Df Sum Sq Mean Sq F value Pr(>F) 2 80 40.00 1.044 0.361 42 1610 38.33 # Here as we can see that p value > 0.05 which implies that all treatments have same mean #*#*****#PART 2#*************

df2 <- data.frame (Temp = round (rnorm (40, 10, 41,0), Sales = round(rnorm ( 40, 9.8, 4), 0)) head (df2) ## ## 1 ## 2 ## 3 ## 4 ## 5 ## 6 Temp Sales 9 14 5 6 10 11 4 12 8 5 3 6 W OBU # Correlation Analysis: cor.test(df 2$ Temp, df 2 $ Sales) ## Pearson's product-moment correlation ## data: df2 $ Temp and df2$ Sales ## t = -0.2276, df = 38, p-value = 0.8212 ## alternative hypothesis: true correlation is not equal to 0 ## 95 percent confidence interval: ## -0.3444475 0.2778053 ## sample estimates: cor ## -0.03689713 # Here we can see that p value > 0.05 which implies that we can say that correlation between both varaibles is 0 # Regression Analysis: m2 <- lm (Sales-Temp, data = df2)

summary (m2) ## Call: ## lm (formula = Sales - Temp, data = df2) ## ## Residuals: ## Min 19 Median 39 Max ## -6.6813 -2.9881 0.3187 2.6256 7.5461 # # * # * # * # ## Coefficients: Estimate Std. Error t value Pr(tl) ## (Intercept) 10.98435 1.59116 6.903 3.32e-08 *** ## Temp -0.037890 .16646 -0.228 0.821 ## --- ## Signif. codes: O '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ''i * # ## Residual standard error: 3.867 on 38 degrees of freedom ## Multiple R-squared: 0.001361, Adjusted R-squared: -0.02492 ## F-statistic: 0.0518 on 1 and 38 DF, p-value: 0.8212

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