## Problem Description

This assignment delves into three Statistical Analysis assignment. Firstly, it explores the strong positive correlation between 10th-grade and 12th-grade science achievement, utilizing a regression model. Secondly, it tests for a significant difference in final exam scores between in-person and Zoom class attendees. Finally, it investigates the influence of geographic location on 12th-grade math achievement using ANOVA. The results provide valuable insights into educational and geographical impacts on academic performance.

## Problem 1:

**Analysis of 10th and 12th Grade Science Achievement
**

In this problem, we conducted an analysis to determine the relationship between 10th-grade science achievement and 12th-grade science achievement. Here are the key findings:

## Model Summary

Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
---|---|---|---|---|

1 | .786a | .618 | .617 | 5.30447 |

**Table 1:** Predictors: (Constant), Tenth Grade Science Achievement

## Coefficientsa

Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
---|---|---|---|---|---|---|

B |
Std. Error |
Beta |
||||

1 | (Constant) | 14.577 | 1.658 | 8.789 | .000 | |

Tenth Grade Science Achievement | .739 | .029 | .786 | 25.233 | .000 |

**Table 2:** Dependent Variable: Twelfth Grade Science Achievement

**Correlation:**The correlation between the two variables is 0.786, indicating a strong positive correlation. This means that higher 10th-grade science achievement is associated with higher 12th-grade science achievement.**Regression Equation:**achsci12 = 14.577 + 0.739 * achsci10**Coefficient of Determination:**The coefficient of determination (R Square) is 0.618, indicating that 61.8% of the variability in the dependent variable (12th-grade science achievement) is explained by the model.**Coefficient of Non-Determination:**The coefficient of non-determination is 0.382, suggesting that 38.2% of the variation in the dependent variable is not explained by the model.

**Fig 1: **Scatterplot with fit line of 12th grade science achievement

## Problem 2:

**Comparison of Exam Scores for Zoom and In-Person Classes
**

This problem involves testing whether there is a significant difference in average scores on a final exam between students who attended classes on Zoom and students who attended classes in person.

### Hypotheses:

**Null Hypothesis (H0):**The average scores on the final exam are the same for both Zoom and in-person classes.**Alternative Hypothesis (Ha):**The average scores on the final exam differ between Zoom and in-person classes.

### Paired Samples Test:

The t-test value is -2.689, and the correlation between Zoom and in-person class scores is 0.359.

Paired Samples Test |
|||||||||
---|---|---|---|---|---|---|---|---|---|

Paired Differences |
t |
df |
Sig. (2-tailed) |
||||||

Mean |
Std. Deviation |
Std. Error Mean |
95% Confidence Interval of the Difference |
||||||

Lower |
Upper |
||||||||

Pair 1 |
Attending in Zoom - Attending in Person | -4.20000 | 4.93964 | 1.56205 | -7.73360 | -.66640 | -2.689 | 9 | .025 |

Paired Samples Correlations |
||||
---|---|---|---|---|

N |
Correlation |
Sig. |
||

Pair 1 |
Attending in Zoom & Attending in Person | 10 | .359 | .308 |

### Paired Samples Test Tables

**Result:
**

The t-test for correlated samples revealed that attending in person produced significantly different average scores in the final exam (M=84.00) compared to students attending on Zoom (M=79.80). The t-statistic is -2.689, and the p-value is less than 0.05, indicating a significant difference.

## Problem 3:

**Impact of Geographic Location on 12th Grade Math Achievement
**

In this problem, we examined the impact of geographic location on 12th-grade math achievement. Here are the key findings:

**Descriptive Statistics:** The mean, standard deviation, and other statistics for 12th-grade math achievement are provided for different geographic regions.

## Descriptives

Twelfth Grade Math Achievement

N | Mean | Std. Deviation | Std. Error | 95% Confidence Interval for Mean | Minimum | Maximum | ||
---|---|---|---|---|---|---|---|---|

Lower Bound | Upper Bound | |||||||

Northeast | 80 | 58.9418 | 7.48700 | .83707 | 57.2756 | 60.6079 | 34.36 | 70.42 |

North Central | 100 | 55.6329 | 8.12019 | .81202 | 54.0217 | 57.2441 | 34.88 | 70.18 |

South | 144 | 56.2279 | 8.07886 | .67324 | 54.8971 | 57.5587 | 35.48 | 70.69 |

West | 75 | 58.1313 | 7.99595 | .92329 | 56.2916 | 59.9710 | 36.57 | 71.05 |

Total | 399 | 56.9807 | 8.03430 | .40222 | 56.1900 | 57.7714 | 34.36 | 71.05 |

**Table 4:** Descriptive statistics- Results of SPSS

**Test of Homogeneity of Variances:** The test indicates that there is homogeneity of variance among the groups.

## Test of Homogeneity of Variances

Levene Statistic |
df1 |
df2 |
Sig. |
||
---|---|---|---|---|---|

Twelfth Grade Math Achievement |
Based on Mean | .707 | 3 | 395 | .548 |

Based on Median | .636 | 3 | 395 | .592 | |

Based on Median and with adjusted df | .636 | 3 | 394.094 | .592 | |

Based on trimmed mean | .752 | 3 | 395 | .522 |

**Table 5:** Test of Homogeneity of Variances

**ANOVA: **An analysis of variance (ANOVA) was performed, showing that there is a significant impact of geographic location on 12th-grade math achievement.

## ANOVA

**Twelfth Grade Math Achievement**

Mean Square |
F |
Sig. |
---|---|---|

223.404 | 3.527 | .015 |

63.344 |

**Table 6:** ANOVA Results

**Conclusion: **Students from different geographic regions have varying levels of math achievement, with the Northeast region having the highest mean score and the North Central region having the lowest. The one-way ANOVA test confirms that these differences are statistically significant.