of A uncw.instructure.com Bob's Burgers is a chain of hamburger fast food restaurants located all over the state of Alaska. Bob, the OM, has collected sales data from their famous Jalapeño Burger over the past 5 weeks, and wants to generate a linear regression equation to estimate potential sales for upcoming weeks. Given the data below, what is the regression equation? Jalapeño Burger Week Sales 354 345 367 3. 380 4. 413 5. Use the Summary Output below to write the equation for the model that explains the linear relationship between weeks and Jalapeño Burger sales? SUMMARY OUTPUT Regression Statistics Multiple R 0.91065 R Square 0.829283 Adjusted R 0.772377 Square Standard 12.67412 Error Observations 5 ANOVA Significance SS MS F. Regression 1. 2340.9 2340.9 14.57294 0.031628 Residual 481.9 160.6333 3. uncw.instructure.com 413 5. Use the Summary Output below to write the equation for the model that explains the linear relationship between weeks and Jalapeño Burger sales? SUMMARY OUTPUT Regression Statistics Multiple R 0.91065 R Square 0.829283 Adjusted R Square 0.772377 Standard 12.67412 Error Observations 5 ANOVA Significance df SS MS Regression 2340.9 6'0 14.57294 0.031628 1. Residual 481.9 160.6333 3. Total 2822.8 4. Standard Upper Lower Upper Coefficients t Stat P-value Lower 95% Error 95% Intercept 325.9 13.29273 24.51716 0.000149 283.5966 368.2034 283.5966 368.2034 X Variable 15.3 4.007909 3.817452 0.031628 2.545045 2805495 2.545045 28.05495 O Y(X) = 325.9X + 15.3 O Y(X) = 15.3X + 325.9 %3D No regression equation can be obtained from the data provided. MacBook Air

Mary used a sample of 68 large U.S. cities to estimate the relationship between Crime (annual property crimes per 100,000 persons) and Income (median annual income per capita, in dollars). Her estimated regression equation was Crime = 428 + 0.050 Income. If Income decreases by 500, we would expect that Crime will Group of answer choices remain unchanged. decrease by 25. increase by 500. decrease by 50.

when a regression is used as a method of predicting dependent variables from one or more independent variables. How are the independent variables different from each other yet related to the dependent variable?

The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 0 1 1.5 2.5 4 5.5 6 Overall Grades 98 86 85 83 80 78 67 Table   Step 1 of 6: Find the estimated slope, y intercept, correlation cofficient  Round your answers to three decimal places.

Can you please check my work

The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for five randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 0 1 3 4 5 Overall Grades 95 92 85 81 62 Table   Step 4 of 6 :  Find the estimated value of y when x=3. Round your answer to three decimal places.     Answer How to enter your answer (opens in new window)

The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 1 2 3 4 4.5 5 5.5 Overall Grades 98 95 93 90 89 72 69 Table Copy Data Step 2 of 6 :   Find the estimated y-intercept. Round your answer to three decimal places.

What's a good topic for a regression topic? Make sure you have two quantitative variables (for example # of siblings and heart rate). Ask for another variable like academic year, major, hobbies, favorite genre of movie. - State the independent and dependent variables and what is the population.

The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for five randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 0 2 3 5 6 Overall Grades 90 89 87 77 61 Table   Step 2 of 6 :  Find the estimated y-intercept. Round your answer to three decimal places.

The following regression equation is based on the analysis of four variables:  SM_DOLLARS is the dollar amount of a watershed conservation agency's weekly spending on social media ads.  RADIO_ADS is the number of radio advertisements aired weekly by the agency.  WS_DOLLARS is the dollar amount of the agency’s weekly spending on web search ads.  The variable WEB_VISITS is the number of weekly visitors to their educational website.  These data have been recorded every week for the past three years.   WEB_VISITS (expected) = 208 + 1.25*SM_DOLLARS + 1.5*RADIO_ADS + 1.2*WS_DOLLARS   The data meet the assumptions for regression analysis, and the regression results, including the coefficients, were found to be statistically significant. Initially, $320 was spent on social media ads, 10 radio ads were aired, and $120 spent on web search ads. How many additional weekly web visits would you predict when the agency increases its weekly spending on social media ads by $440 without changing the…