ect the null hypoth rence in the amou rocessed food at h vidence to reject th val for the data? P

MATLAB: An Introduction with Applications
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Author:Amos Gilat
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Chapter1: Starting With Matlab
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### Statistical Analysis Activity

#### 1a) Formulate Hypotheses
The null hypothesis (H0) is that the difference in the grams of sugar between ultra-processed and processed food is zero (µ = 0).
- **H0**: µ = 0
- **Example alternative hypothesis (H1)**: The mean (µ) difference in grams of sugar between ultra-processed foods (µ_up) and processed foods (µ_p) is not zero. That is \( H1: \mu_{up} \neq \mu_{p} \).

#### 1b) Testing the Hypothesis with Sample Data
Generate 1,000 samples. Assume that students in our class were asked to calculate the difference in the amount of sugar between their ultra-processed food and processed food at home. The mean (µ) sugar difference was found to be 14.75 grams. Do we have evidence to reject the null hypothesis?

#### 1c) Different Sample Value
Assume now that your sample difference in the amount of sugar between ultra-processed food and processed food at home was 4.75 grams. Do you have evidence to reject the null hypothesis with this sample data?

#### 1d) Confidence Interval Calculation
Calculate and interpret a 92% confidence interval for the data obtained. Express the interpretation using the full confidence interval language (e.g., "I am 92% confident that…").

#### 1e) Data Distribution Analysis
Determine what percent of the data lie to the left of -14.25 grams of sugar.

### Relevant Concepts
This section covers key concepts in hypothesis testing, such as formulating null and alternative hypotheses, the importance of sample sizes, calculating and interpreting confidence intervals, and understanding normal distribution.

No graphs or diagrams are included in this activity, so explanations are based solely on textual data.
Transcribed Image Text:### Statistical Analysis Activity #### 1a) Formulate Hypotheses The null hypothesis (H0) is that the difference in the grams of sugar between ultra-processed and processed food is zero (µ = 0). - **H0**: µ = 0 - **Example alternative hypothesis (H1)**: The mean (µ) difference in grams of sugar between ultra-processed foods (µ_up) and processed foods (µ_p) is not zero. That is \( H1: \mu_{up} \neq \mu_{p} \). #### 1b) Testing the Hypothesis with Sample Data Generate 1,000 samples. Assume that students in our class were asked to calculate the difference in the amount of sugar between their ultra-processed food and processed food at home. The mean (µ) sugar difference was found to be 14.75 grams. Do we have evidence to reject the null hypothesis? #### 1c) Different Sample Value Assume now that your sample difference in the amount of sugar between ultra-processed food and processed food at home was 4.75 grams. Do you have evidence to reject the null hypothesis with this sample data? #### 1d) Confidence Interval Calculation Calculate and interpret a 92% confidence interval for the data obtained. Express the interpretation using the full confidence interval language (e.g., "I am 92% confident that…"). #### 1e) Data Distribution Analysis Determine what percent of the data lie to the left of -14.25 grams of sugar. ### Relevant Concepts This section covers key concepts in hypothesis testing, such as formulating null and alternative hypotheses, the importance of sample sizes, calculating and interpreting confidence intervals, and understanding normal distribution. No graphs or diagrams are included in this activity, so explanations are based solely on textual data.
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