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Statistics - Essay Example

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A company is making promotional cups as convention give away prizes. The cups should receive 3.5 grams of paint for the logo so that it is heavy enough to last a few years of regular wear but not so heavy that it begins to become too expensive to produce. Assume that all…
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2. You wish to estimate the weight of the students at EKU. Using the question 2 data, calculate your sample mean to the nearest tenth of a pound. Use that estimate to construct 95% confidence interval for the true average if you know that the standard deviation of the population is 2.3 pounds. Construct 95% confidence interval if you do not know the true standard deviation. Comment on why those confidence intervals are different. Both confidence intervals are slightly different because one is computed based on known population standard deviation, σ and z distribution and other is calculated based on sample standard deviation, s and t distribution.

The t distribution has slightly broader tails then z distribution, therefore, the confidence interval is also slightly wider as compared to z distribution. 3. The average height in the United States for men is 69.3 inches. The standard deviation for the population of the men in the US is 3.4 inches. You measure a sample of people from Richmond and find that the average of these 16 men is 71.4 inches with a sample standard deviation of 4.8 inches. Assume all testing is for 95% confidence. 4. We wish to compare the weights of the people of Richmond, Kentucky with those of Lexington, Kentucky.

We do not know the standard deviation for the population as a whole and we cannot assume that the unknown sigma is the same for each city. We take a sample from each city and the results are as follows All the three models are statistically significant and also, In model 3, individually, Parent’s income and Years of education significantly predicts person’s annual income . However, the adjusted R2 value for the model 3 is highest of all the regression models. Therefore, the best model is given by Adjusted R2 is a measure of Goodness-of-fit and penalizes the R2 for adding additional explanatory variables.

The higher the value of adjusted R2,

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