Question

Linear Regression Stats Question Consider maximum temperatures in January at Seattle (X) and Chicago (Y), (in...

Linear Regression Stats Question

Consider maximum temperatures in January at Seattle (X) and Chicago (Y), (in data.R file). Assuming that X and Y are related according to a linear regression model: (a) Find the estimated regression line. What is the average maximum temperature at Chicago for 65*F at Seattle? (b) Construct a 95% CI for the slope of the regression line. (c) Construct a 95% CI for the average maximum temperature at Chicago when x=23. (d) Construct a 95% CI for the maximum temperature at Chicago when x = 23.

data from file:

x <- c(33,32,30,29,25,30,37,37,29,30,36,32,33,34,53,45,25,28,32,27,26,28,24,26,9,22,17,26,27,30,34)
y <- c(34,36,30,29,30,35,44,38,31,33,39,33,34,39,51,44,25,34,36,29,27,29,27,24,11,21,19,26,28,31,38)

Please give answer in R code! Will upvote answer

Homework Answers

Answer #1

C and D gives you the confidence interval for x=23 and predict the confidence interval.​

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
In the simple linear regression model estimate Y = b0 + b1X A. Y - estimated...
In the simple linear regression model estimate Y = b0 + b1X A. Y - estimated average predicted value, X – predictor, Y-intercept (b1), slope (b0) B. Y - estimated average predicted value, X – predictor, Y-intercept (b0), slope (b1) C. X - estimated average predicted value, Y – predictor, Y-intercept (b1), slope (b0) D. X - estimated average predicted value, Y – predictor, Y-intercept (b0), slope (b1) The slope (b1) represents A. the estimated average change in Y per...
In a simple linear regression analysis attempting to link lottery sales (y) to jackpot amount (x),...
In a simple linear regression analysis attempting to link lottery sales (y) to jackpot amount (x), the following data are available: x y jackpot ($millions) Sales (millions) 12 60 14 70 6 40 8 50 The slope (b) of the estimated regression equation here is 3.5. The intercept (a) is 20. Produce the 95% confidence interval estimate of the population slope, β, and report the upper bound for the interval. a)5.02 b)4.66 c)7.23 d)3.72
QUESTION 21 A linear model is found to be y = 3.5x + 6. At a...
QUESTION 21 A linear model is found to be y = 3.5x + 6. At a value of x = 4, the equation yields y = 20. However, the actual data value for x =4 is found to be y = 22.3. The residual at x = 20 is a. 3.5 b. 6 c. 20 d. 22.3 e. 2.3 QUESTION 22 A regression equation is found to be y=2x+10. The prediction for the dependent variable when the independent variable is...
Consider a linear regression model where y represents the response variable, x is a quantitative explanatory...
Consider a linear regression model where y represents the response variable, x is a quantitative explanatory variable, and d is a dummy variable. The model is estimated as  yˆy^  = 15.4 + 3.6x − 4.4d. a. Interpret the dummy variable coefficient. Intercept shifts down by 4.4 units as d changes from 0 to 1. Slope shifts down by 4.4 units as d changes from 0 to 1. Intercept shifts up by 4.4 units as d changes from 0 to 1. Slope shifts...
Fitting the simple linear regression model to the n= 27 observations on x = modulus of...
Fitting the simple linear regression model to the n= 27 observations on x = modulus of elasticity and y = flexural strength given in Exercise 15 of Section 12.2 resulted in yˆ = 7.592, sYˆ = .179 when x = 40 and yˆ = 9.741, sYˆ = .253 for x = 60. a. Explain why sY ˆ is larger when x = 60 than when x = 40. b. Calculate a confidence interval with a confidence level of 95% for...
1) In the regression equation, y = 2.164 + 1.3657x, n = 6, the mean of...
1) In the regression equation, y = 2.164 + 1.3657x, n = 6, the mean of x is 8.667, SSxx = 89.333 and Se = 3.44. A 95% confidence interval for the average of y when x = 8 is _________. 2)If the correlation coefficient between variables X and Y is roughly zero, then ______. 3) Determine the Pearson product-moment correlation coefficient for the following data. x 1 11 9 6 5 3 2 y 10 4 4 5 7...
I need to show the linear regression of this. Y: temperature goes up X: ice cream...
I need to show the linear regression of this. Y: temperature goes up X: ice cream sales go up I work in an ice cream shop and it seems as the temperature rises in the summer months so do the ice cream sales. The temperature rising will be my independent variable. (?) Data: Month - Average Temp. - Gallons of Ice Cream January - 25 degrees - 167 gallons February - 32 degrees - 176 gallons March - 37 degrees...
Below is a regression using X = average price, Y = units sold, n = 20...
Below is a regression using X = average price, Y = units sold, n = 20 stores.      R2 0.200     Std. Error 26.128     n 20     ANOVA table   Source SS df MS F p-value   Regression 3,080.89     1        3,080.89 4.51      .0478       Residual 12,288.31     18        682.68   Total 15,369.20     19          Regression output confidence interval   variables coefficients std. error t (df = 18) p-value 95% lower 95% upper   Intercept 614.9300     51.2343     12.002      .0000   ...
Question 8 The common cricket can be used as a crude thermometer. The colder the temperature,...
Question 8 The common cricket can be used as a crude thermometer. The colder the temperature, the slower the rate of chirping. The table below shows the average chirp rate of a cricket at various temperatures. Chirp Rate (chirps/second) Temperature (ºF) 2.5 52.2 2.9 61.4 4 74.5 2.2 53.3 3.7 70.9 2.6 61.7 Compute the least-squares regression line for predicting the temperature from the chirp rate. Question 8 options: a) y = 12.3889 + 26.2719x b) y = 12.0876 +...
1. Given the following observations of quantitative variables X and Y: x= 0, 1, 2, 3,...
1. Given the following observations of quantitative variables X and Y: x= 0, 1, 2, 3, 15 y= 3, 4, 6, 10, 0 a. Make a scatterplot of the data on the axes. Circle the most influential observation. (4 points)    (b)   Determine the LSRL of Y on X. Draw this line carefully on your scatterplot. (4 points) (c)   What is the definition of a regression outlier? (4 points) (d) Which data point is the biggest regression outlier? (4 points)...