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Statistics

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Statistical treatments are techniques utilized to analyze and interpret data. Some of the most commonly used statistical treatments include t-tests, ANOVA, chi-square, and regression analysis. T-tests determine if two sets of data means are significantly different, while ANOVA analyzes differences between three or more groups. Chi-square determines significant associations between two categorical variables, while regression analysis determines the relationship between a dependent variable and one or more independent variables. These treatments are vital in helping researchers and analysts draw conclusions from their data.

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First Exam

t-test

September 24, 2022

Name:

Nature of work: Administrative Assistant II

1. Paid days off and the single sample t test: The number of paid days off (e.g., vacation, sick leave)
taken by eighteen regular appointed employees at few school district agencies which is compared to
the national average. The human resource personnel assist to help her determine how many paid
days off she should provide. In general, they want to set some standards for the employees according
to the civil service issuances and guidelines. Let’s assume it was recorded by HR personnel on paid
days off leaves with the impression that the national average is 15 days. The data for the eighteen
regular appointed employees during the last fiscal year are listed below. Assume the population to be
normal and use .05 level of significance to test the hypothesis.

10 12 15
11 27 13
8 5 7
14 6 9
13 10 14
12 12 12


2. At the end of the calendar year, the school head initiate to measure the performance rating of the
non-teaching employees. She asked her assistant to record their performance rating on the previous
years, for the year 2020 and 2021. The following data represents the 2020 and 2021 performance
rating of selected non-teaching personnel. Assume the population to be approximately normal with
equal variances.
PERFORMANCE RATING
2020 2021
Male Female Male Female
4.20 4.00 4.10 4.00
4.00 4.20 4.00 4.20
3.90 4.50 4.00 4.50
4.00 4.50 4.00 4.50
3.50 4.60 4.00 4.67
4.10 4.30 4.23 4.30
4.20 4.25 4.24 4.26
4.21 4.22 4.50 4.51
4.60 4.23 4.56 4.24
4.50 4.60 4.70 4.75
4.00 4.00 4.80 4.10
4.10 4.30 4.50 4.51
4.40 4.50 4.60 4.50
4.30 4.20 4.00 4.20
4.10 4.00 4.10 4.00
3.80 3.90 3.92 3.50
4.30 4.40
4.70 4.60

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