Chapter 12, Problem 12.5AC

The largest commercial fishing enterprise in the southeastern United States is the harvest of shrimp. In a study, researchers monitored variables thought to be related to the abundance of white shrimp. One variable the researchers thought might be related to abundance is the amount of oxygen in the water. The relationship between mean catch per tow of white shrimp and oxygen concentration was described by fitting a regression line using data from ten randomly selected offshore sites. (The "catch" per tow is the number of shrimp caught in a single outing.) Computer output is shown below. The regression equation is Mean catch per tow = -5855 + 97.4 O2 Saturation Predictor Coef SE Coef T P Constant -5855 2393 -2.45 0.040 O2 Saturation 97.4 34.62 2.81 0.023       c)Construct a 95% confidence interval for ?. (Use a table or technology. Round your answers to three decimal places.)   d)What margin of error is associated with the confidence interval in part (c)? (Round your…

A music critic was interested in whether particular variables measured on a song change over time. Two variables the critic considered were a song’s Tempo (in bpm) and a song’s Danceability. We will use the songs written before the year 2000 from the original SpotifySample data set. The data set that you will use to complete this investigation is called SpotifyB2000 and consists of 483 songs. Write the least-squares regression line equation describing Year and Danceability usingproper notation and values. Interpret the slope of the regression line for Year and Danceability in context.  Would the interpretation of the y-intercept for Year and Danceability be meaningful? Ifso, interpret it. If not, state why not in one sentence. Calculate and record the coefficient of determination value r2for Year and Danceabilityand interpret this value in context. State the hypotheses for the test of the slope. Write the p-value found in the output from (n), and use the p-value provided in the…

please do with rstudio and provide all the codes.

A 10-year study conducted by the American Heart Association provided data on how age, blood pressure, and smoking related to the risk of strokes. The data file “Statsdata2.xslx” includes a portion of the data from the study. The variable “Risk of Stroke” is measured as the percentage of risk (proportion times 100) that a person will have a stroke over the next 10-year period.  Use Excel’s Data Analysis tool to conduct a simple linear regression between Risk of Stroke and Age. Choose confidence level at 95%. Please copy and paste the regression analysis output below.  1. Based on the simple regression analysis output, write the estimated regression equation.  2. If we choose the significance level α=0.05, is Age a significant factor in explaining the risk of a stroke? Explain.   3.  What is the coefficient of determination (R-square)? How to interpret R-square?     Risk of Stroke (%) Age Pressure Smoker (Yes=1) 12 57 152 0 24 67 163 0 13 58 155 0 56 86 177 1 28 59 196 0…

Please ASAP in part a , b and c

Look at the picture of the data set attached below or click this link to view the data set: https://docs.google.com/spreadsheets/d/1e0EJHxWj19KitPwekp2ncj7zhNOGjNRr5qJPfzZaosA/edit?usp=sharing Then look at the regression analysis and explain what the results mean.

Find the degrees of freedom in a regression model with an intercept term that has 88 observations and 7 explanatory variables.

Isla Esme, Inc., sells tea products to various customers. In recent years, profits have been declining. The CFO of the business investigated the reasons for the profit decline and performed regression analysis for sales and costs. He determined that sales depend on product price, delivery speed, customer services, and marketing expenses. He also determined that total costs consist of variable costs of $25 per unit and fixed costs of $56,000. Marketing expenses have a coefficient of determination of 75% related sales.Question: Define the coefficient of determination and explain what it means in this scenario.

A consumer advocacy group recorded several variables on 140 models of cars. The resulting information was used to produce the following regression output that relates the city gas mileage (in mpg) and the engine displacement (in cubic inches).The regression equation ismpg_city= 33.1 - 0.07 * displacementWe have a car that has an engine with 136 cubic inches. Based on this output, what city gas mileage would you predict for this car?