# Predicting GPA from ACT Scores and Analyzing Crime Rate vs. Education with SAS

August 04, 2023
Lily Rivera
🇺🇸 United States
SAS
Lily Rivera is a SAS assignment expert with over 10 years of experience in data analysis and programming. She holds a master's degree in Statistics from George Washington University & has a track record of delivering high-quality SAS assignments on time.
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Key Topics
• Problem Description
• Part I: Confidence Interval for Slope (β_1)
• Part II: Hypothesis Testing for Significance of ACT on GPA
• Part III: P-Value Interpretation
• Part IV: Confidence Interval and Prediction Interval for ACT = 28
• Part V: Comparison of Confidence and Prediction Intervals
• Part VI: Plot Analysis
• Part VII: ANOVA (Analysis of Variance)
• Part VIII: MSR and MSE
• Part IX: Hypothesis Testing Using F-Test
• Part X: R-squared Interpretation
• Part XI: Second Question - Crime Rate and Education
• Code:
• Conclusion

In the engaging exploration, we delve into the fascinating world of statistical analysis. We rigorously test the hypothesis that GPA can be effectively predicted from ACT scores, shedding light on the essential role of standardized testing in educational outcomes. Furthermore, we venture into the intriguing realm of crime rates and education, utilizing SAS to uncover significant relationships. This comprehensive analysis not only broadens our understanding of these critical variables but also showcases the power of statistical tools in making data-driven decisions.

## Problem Description

In this SAS assignment, we aim to investigate whether a high school student's Grade Point Average (GPA) can be predicted based on their ACT scores. This involves a comprehensive statistical analysis. The central question is: Can we reliably predict GPA from ACT scores? To address this question, we will perform various statistical tests and analyses.

## Part I: Confidence Interval for Slope (β_1)

To begin, we calculate the confidence interval for the slope (β_1) with a 99% confidence level. This confidence interval helps us understand the range within which we can expect the true slope of the regression to lie. The calculation involves the standard error of the slope (se(β_1)).

The confidence interval for β1

Where:

The results are as follows:

Parameter Estimates
VariableLabelDFParameterEstimateStandardErrort ValuePr > |t|99% Confidence Limits
InterceptIntercept12.114050.320896.59<.00011.273902.95420
ACTACT10.038830.012773.040.00290.005390.07227

Table 1:Parameter Estimates Table

Interpretation: The 99% confidence interval for the slope (β_1) is between 0.00539 and 0.07227. This means we are 99% confident that, for the population, a one-unit increase in ACT scores will result in a GPA increase within this range.

## Part II: Hypothesis Testing for Significance of ACT on GPA

We formulate the following hypotheses:

• Null Hypothesis (H0): β_1 = 0 (ACT has no significant effect on GPA).
• Alternative Hypothesis (H1): β_1 ≠ 0 (ACT has a significant effect on GPA).

The test statistic (t) is calculated as follows:

 Test Statistics t 3.04

Table 2: Test Statistics

Decision Rule: If the test statistic is greater than the critical value (t0.01/2, 118), we reject the null hypothesis. The critical value at the 1% significance level is 2.618.

Conclusion:The test statistic (3.04) is greater than the critical value (2.618), so we reject the null hypothesis. This indicates that ACT scores have a significant effect on GPA.

## Part III: P-Value Interpretation

The p-value for this test is 0.0029. A p-value less than the significance level (0.01) leads to the rejection of the null hypothesis. This p-value aligns with our previous hypothesis test, providing further evidence that ACT scores significantly affect GPA.

## Part IV: Confidence Interval and Prediction Interval for ACT = 28

Next, we calculate the 95% confidence interval for the GPA of students with an ACT score of 28. The confidence interval is:

Y ̂_h interval equation

Predicted ValueStd Error95% Confidence Limits
3.20120.07063.0614 to 3.341

Table 3:95% Confidence Limits Table (for confidence interval)

Interpretation:We are 95% confident that the average GPA for students with an ACT score of 28 in the population lies between 3.0614 and 3.341.

Additionally, we calculate a 95% prediction interval for an individual student with an ACT score of 28, resulting in the following interval:

Predicted ValueStd Error95% Prediction Limits
3.20120.07061.9594 to 4.4431

Table 4: 95% Prediction Limits Table (for prediction interval)

Interpretation: We are 95% confident that an individual's GPA, given an ACT score of 28, will fall within the range of 1.9594 to 4.4431.

## Part V: Comparison of Confidence and Prediction Intervals

It's important to note that the prediction interval is wider than the confidence interval, as the prediction interval accounts for individual variation, including the error term (MSE).

## Part VI: Plot Analysis

A plot illustrating the fit line with the confidence interval demonstrates that the confidence interval is relatively narrow, suggesting low uncertainty in the prediction.

Fig 1: Fit Plot for GPA

## Part VII: ANOVA (Analysis of Variance)

An ANOVA analysis was conducted to assess the overall significance of the model. The results are as follows:

SourceDFSum of SquaresMean SquareF ValuePr > F
Model13.587853.587859.240.0029
Error11845.817610.38828
Total11949.40545

Table 5:Analysis of Variance Table

This analysis confirms that the model is statistically significant, with a small p-value (0.0029), indicating a significant relationship between ACT scores and GPA.

## Part VIII: MSR and MSE

MSR (Mean Square due to Regression) and MSE (Mean Square due to Error) were discussed. These metrics help in understanding the variance explained by the model and the unexplained variance, respectively.

## Part IX: Hypothesis Testing Using F-Test

We conducted an F-test to assess the significance of β_1. The results are as follows:

 Test Statistics F 9.24

Table 6: Test Statistics Table

Decision Rule: If F-calculated (9.24) is greater than the critical value of F at the 1% significance level and (1, 118) degrees of freedom (6.58), we reject the null hypothesis.

Conclusion: We conclude that β_1 is significantly different from 0, indicating a significant linear relationship between ACT scores and GPA.

## Part X: R-squared Interpretation

The absolute magnitude of the reduction in the variation of Y when X (ACT) is introduced into the regression model is 3.588. The relative magnitude, R-squared, is calculated as 0.0726.

The null and alternative hypothesis is

The test statistics is given by

The test statistics value is 9.24

## Part XI: Second Question - Crime Rate and Education

In the second part of the assignment, we analyze the relationship between crime rate and education, focusing on a full model and a reduced model. We perform ANOVA tests to determine the significance of the relationship between crime rate and education.

r=√0.0726

r=+0.27

Q2

The full model is

The reduced model is

Full Model

Analysis of Variance
SourceDFSum ofSquaresMeanSquareF ValuePr > F
Model299107366495536838.930.0003
Error814496287425550972
Corrected Total83548736108

Table 7:Analysis of Variance – Full Model

Reduced Model

Analysis of Variance
SourceDFSum ofSquaresMeanSquareF ValuePr > F
Model1934629429346294216.83<.0001
Error824552731655552112
Corrected Total83548736108

Table 8: Analysis of Variance- Reduced Model

SSE(F)= 449628742

SSE(R) = 455273165

DF(F)=n-k-1=84-2-1=81

DF(R)=n-k-1=84-1-1=82

Decision rule: if the F-stat is less than critical value of F at 1% significance level and (1,81) df, we reject the null hypothesis. Since the F-stat (1.017) is less than the critical value (6.96) we cannot reject the null hypothesis that the relationship between crime rate and percentage of high school graduates is not linear. We conclude that a linear relationship between crime rate and percentage of high school graduates.

## Code:

The provided code is used for regression analysis, plotting, and ANOVA tests. It serves as the practical implementation of the statistical analyses discussed in the assignment.

``````im proc reg data=WORK.IMPORT2 alpha=0.01 plots(only)=(diagnostics residuals
fitplot observedbypredicted);
model GPA=ACT / clb;
run;
quit;

data c;
input ACT;
cards;
28
;

data new;
set WORK.IMPORT2 c;
run;

proc reg data=new plots=(NONE);
model GPA=ACT/clm cli;
output out=Pred p=P;   /* predicted values for the scoring data */
quit;

proc print data=Pred;
run;

proc reg data=WORK.IMPORT2 alpha=0.05 plots(only)=(diagnostics residuals
fitplot observedbypredicted);
model GPA=ACT / clb;
run;
quit;

/*2*/
proc reg data=WORK.IMPORT4 alpha=0.01 plots(only)=(diagnostics residuals
fitplot observedbypredicted);
model Crime=Edu / clb;
run;
quit;

model Crime=Edu Edu*Edu / showpvalues selection=none;
run;

proc reg data=Work.reg_design alpha=0.01 plots(only)=(diagnostics residuals
observedbypredicted);
ods select ParameterEstimates DiagnosticsPanel ResidualPlot
ObservedByPredicted;
model Crime=_GLSMOD / clb;
run;
quit;

proc delete data=Work.reg_design;
run;
``````

## Conclusion

In conclusion, this assignment explores the intricate relationship between variables, especially the impact of ACT scores on GPA. The analyses and results provide a comprehensive understanding of how statistical tools can be utilized to make informed predictions and decisions

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