Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 3m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample1h 1m
- 10. Hypothesis Testing for Two Samples2h 8m
- 11. Correlation48m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
2. Describing Data with Tables and Graphs
Frequency Distributions
Problem 2.1.28
Textbook Question
Births Natural births randomly selected from four hospitals in New York State occurred on the days of the week (in the order of Monday through Sunday) with these frequencies: 52, 66, 72, 57, 57, 43, 53. Does it appear that such births occur on the days of the week with equal frequency?

1
Step 1: Define the null hypothesis (Hâ‚€) and the alternative hypothesis (Hâ‚). The null hypothesis states that births occur with equal frequency across all days of the week, while the alternative hypothesis states that births do not occur with equal frequency.
Step 2: Calculate the expected frequency for each day of the week under the assumption of equal frequency. Since there are 7 days in a week and the total number of births is the sum of the given frequencies, divide the total number of births by 7 to find the expected frequency for each day.
Step 3: Use the chi-square test formula to calculate the test statistic. The formula is χ² = Σ((Oᵢ - Eᵢ)² / Eᵢ), where Oᵢ represents the observed frequency for each day and Eᵢ represents the expected frequency for each day. Perform this calculation for all 7 days and sum the results.
Step 4: Determine the degrees of freedom for the chi-square test. The degrees of freedom are calculated as (number of categories - 1). In this case, there are 7 days, so the degrees of freedom are 7 - 1 = 6.
Step 5: Compare the calculated chi-square test statistic to the critical value from the chi-square distribution table at the chosen significance level (e.g., α = 0.05) and degrees of freedom. If the test statistic exceeds the critical value, reject the null hypothesis; otherwise, fail to reject the null hypothesis.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Chi-Square Test of Goodness of Fit
The Chi-Square Test of Goodness of Fit is a statistical method used to determine if observed frequencies differ from expected frequencies. In this context, it helps assess whether the number of births across the days of the week is uniformly distributed or if certain days have significantly more or fewer births than expected.
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Step 2: Calculate Test Statistic
Null Hypothesis
The null hypothesis is a statement that assumes no effect or no difference, serving as a starting point for statistical testing. In this scenario, the null hypothesis would state that births occur with equal frequency across all days of the week, which can be tested against the observed data.
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Step 1: Write Hypotheses
Expected Frequencies
Expected frequencies are the theoretical frequencies that would occur if the null hypothesis were true. For this question, if births are equally likely on each day, the expected frequency for each day can be calculated by dividing the total number of births by the number of days (7), providing a benchmark for comparison with the observed frequencies.
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