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Imagine that you’re building a model to predict a student’s exam score based on the number of hours that they studied. You want to add the location that they studied as a variable into the model, with 3 eligible values: dorm, library, coffee shop.
How many dummy variables need to be added into the regression to represent the student’s study location?
Imagine that you’re building a model to predict a student’s exam score based on the number of hours that they studied. You want to add the location that they studied as a variable into the model, with 3 eligible values: dorm, library, coffee shop.
With “dorm” as the baseline for your study location dummy variables, you build the following multi-linear regression equation:
y-hat = 23.82 + 6.7(Hours Studied) + 9.83(Library) – 3.42(Coffee Shop)
Which answer choice(s) below correctly interpret the dummy variables in this regression equation?
Your university wants to predict the “Sign-Ups” at a student organization receives at mega fair based on the social media “Posts” made by that organization throughout the semester. They randomly sample 30 student organizations, resulting in the following regression analysis.
Find the 95% confidence interval for the true slope of β_{Posts.}
We are % confident that the true population parameter is between and .
(When calculating test statistic, round to intermediary values to 3 decimal places. Round bounds to 3 decimal places.)
Your university wants to predict the “Sign-Ups” at a student organization receives at mega fair based on the social media “Posts” made by that organization throughout the semester. They randomly sample 30 student organizations, resulting in the following regression analysis.
Is there a meaningful, linear relationship between “Sign-Ups” and “Posts”? Provide support for your claim using a hypothesis test with an alpha level of 0.05.
Since our p-value of is (less / greater) than than our alpha level of , we (reject / fail to reject) the null hypothesis and (do / do not) have enough evidence to support the alternative hypothesis.
Your university wants to predict the “Sign-Ups” at a student organization receives at mega fair based on the social media “Posts” made by that organization throughout the semester. They randomly sample 30 student organizations, resulting in the following regression analysis.
Is there a meaningful, linear relationship in this model? Provide support for your claim using a hypothesis test with an alpha level of 0.05.
Since our p-value of is (less / greater) than than our alpha level of , we (reject / fail to reject) the null hypothesis and (do / do not) have enough evidence to support the alternative hypothesis.
Complete the below time series table with a 3 point moving average. Round values to 2 decimal places if needed.
Mon | Tue | Wed | Thu | |
---|---|---|---|---|
Steps Walked | 7800 | 12300 | 15900 | 13800 |
Estimated (y-tilde) | – | – | ||
Predicted (y-hat) | – | – | – |
Given the below time series, with a 3 point moving average, predict the number of steps that’ll be walked on Friday.
Mon | Tue | Wed | Thu | |
---|---|---|---|---|
Steps Walked | 7800 | 12300 | 15900 | 13800 |
Estimated (y-tilde) | – | – | ??? | ??? |
Predicted (y-hat) | – | – | – | ??? |
Complete the below time series table with exponential smoothing, given ⍺ = 0.8. Round values to 2 decimal places if needed.
Mon | Tue | Wed | Thu | |
---|---|---|---|---|
Steps Walked | 7800 | 12300 | 15900 | 13800 |
Estimated (y-tilde) | ||||
Predicted (y-hat) | – |
Given the below time series, with ⍺ = 0.8, predict the number of steps that’ll be walked on Friday.
Mon | Tue | Wed | Thu | |
---|---|---|---|---|
Steps Walked | 7800 | 12300 | 15900 | 13800 |
Estimated (y-tilde) | – | ??? | ??? | ??? |
Predicted (y-hat) | – | – | ??? | ??? |
Given the below time series, calculate MSE.
Fall ’23 | Spring ’24 | Fall ’24 | Spring ’25 | Fall ’25 | Spring ’26 | |
---|---|---|---|---|---|---|
Campus Parties Thrown | 45 | 50 | 52 | 55 | 60 | 70 |
Estimated (y-tilde) | – | – | 49 | 52.3 | 55.7 | 61.7 |
Predicted (y-hat) | – | – | – | 49 | 52.3 | 55.7 |
Given the below time series, calculate MAD.
Fall ’23 | Spring ’24 | Fall ’24 | Spring ’25 | Fall ’25 | Spring ’26 | |
---|---|---|---|---|---|---|
Campus Parties Thrown | 45 | 50 | 52 | 55 | 60 | 70 |
Estimated (y-tilde) | – | – | 49 | 52.3 | 55.7 | 61.7 |
Predicted (y-hat) | – | – | – | 49 | 52.3 | 55.7 |
Given the below time series, calculate MAPE.
Fall ’23 | Spring ’24 | Fall ’24 | Spring ’25 | Fall ’25 | Spring ’26 | |
---|---|---|---|---|---|---|
Campus Parties Thrown | 45 | 50 | 52 | 55 | 60 | 70 |
Estimated (y-tilde) | – | – | 49 | 52.3 | 55.7 | 61.7 |
Predicted (y-hat) | – | – | – | 49 | 52.3 | 55.7 |
Given the following regression output, use the additive vs. multiplicative model to write the regression equation.
Given the following regression output, use the additive vs. multiplicative model to write the regression equation.
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