A gym owner wants to know if the gym has similar numbers of members across different age groups. The table shows the distribution of ages for members from a random survey. Using x2 = 0.92 & α = 0.05, test the claim that the gym has equal numbers of members of all age ranges.
Table of contents
- 1. Intro to Stats and Collecting Data1h 14m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically2h 5m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables3h 6m
- 6. Normal Distribution and Continuous Random Variables2h 11m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 23m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals15m
- Confidence Intervals for Population Mean1h 18m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - Excel28m
- Confidence Intervals for Population Means - Excel25m
- 8. Sampling Distributions & Confidence Intervals: Proportion2h 10m
- 9. Hypothesis Testing for One Sample5h 8m
- Steps in Hypothesis Testing1h 6m
- Performing Hypothesis Tests: Means1h 4m
- Hypothesis Testing: Means - Excel42m
- Performing Hypothesis Tests: Proportions37m
- Hypothesis Testing: Proportions - Excel27m
- Performing Hypothesis Tests: Variance12m
- Critical Values and Rejection Regions28m
- Link Between Confidence Intervals and Hypothesis Testing12m
- Type I & Type II Errors16m
- 10. Hypothesis Testing for Two Samples5h 37m
- Two Proportions1h 13m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 3m
- Two Means - Unknown Variances Hypothesis Test - Excel12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - Excel9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - Excel21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - Excel12m
- Two Variances and F Distribution29m
- Two Variances - Graphing Calculator16m
- 11. Correlation1h 24m
- 12. Regression3h 33m
- Linear Regression & Least Squares Method26m
- Residuals12m
- Coefficient of Determination12m
- Regression Line Equation and Coefficient of Determination - Excel8m
- Finding Residuals and Creating Residual Plots - Excel11m
- Inferences for Slope31m
- Enabling Data Analysis Toolpak1m
- Regression Readout of the Data Analysis Toolpak - Excel21m
- Prediction Intervals13m
- Prediction Intervals - Excel19m
- Multiple Regression - Excel29m
- Quadratic Regression15m
- Quadratic Regression - Excel10m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA2h 28m
13. Chi-Square Tests & Goodness of Fit
Goodness of Fit Test
Problem 10.1.5
Textbook Question
Finding Expected Frequencies
In Exercises 3–6, find the expected frequency for the values of n and pᵢ.
n=230, pᵢ=0.25
Verified step by step guidance1
Step 1: Understand the concept of expected frequency. Expected frequency is calculated using the formula: E = n × pᵢ, where 'n' is the total number of observations and 'pᵢ' is the probability of the specific category or event.
Step 2: Identify the values given in the problem. Here, n = 230 (total number of observations) and pᵢ = 0.25 (probability of the specific category).
Step 3: Substitute the values into the formula for expected frequency. Using MathML, the formula is: . Substituting, it becomes: .
Step 4: Perform the multiplication operation to calculate the expected frequency. Multiply 230 by 0.25 to find the result.
Step 5: Interpret the result. The expected frequency represents the number of occurrences you would expect for the specific category given the total observations and probability.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Expected Frequency
Expected frequency refers to the anticipated number of occurrences of a particular outcome in a statistical experiment, calculated by multiplying the total number of trials (n) by the probability of the outcome (pᵢ). In this case, it helps in determining how many times we expect a specific event to happen based on the given probability.
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Contingency Tables & Expected Frequencies
Probability
Probability is a measure of the likelihood that a particular event will occur, expressed as a number between 0 and 1. In the context of this question, pᵢ represents the probability of a specific outcome occurring in a sample of size n, which is crucial for calculating expected frequencies.
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Introduction to Probability
Sample Size (n)
Sample size (n) is the total number of observations or trials in a statistical study. It is essential for determining the expected frequency, as a larger sample size can lead to more reliable estimates of probabilities and outcomes, thereby affecting the expected frequency calculation.
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