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Predictions and Residual Plots

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Wize University Statistics Textbook > Inference for Linear Regression

Simple Linear Regression Analysis

3 Activities

Wize University Statistics Textbook > Simple Linear Regression

Predictions and Residual Plots

8 Activities


The equation y^\hat{y}=35+1.50xx is used to predict the hourly wage yy of someone with xx years of experience.

(c) Elizabeth has 4 years of experience and earns $36. What is the residual?



More Simple Linear Regression Analysis Questions:
Linear Regression
Parts (a) to (c) are based on the following information.
The equation y^\hat{y}=35+1.50xx is used to predict the hourly wage yy of someone with xx years of experience. The correlation is r=0.56r=0.56. Suppose that xx is changed to months of experience.
(a) Which of the following will be the correct regression line?
Linear Regression
Parts (a) to (c) are based on the following information.
The equation y^\hat{y}=35+1.50xx is used to predict the hourly wage yy of someone with xx years of experience. The correlation is r=0.56r=0.56. Suppose that xx is changed to months of experience.
(a) Which of the following will be the correct regression line?
Linear Regression
Parts (a) to (c) are based on the following information.
The equation y^\hat{y}=35+1.50xx is used to predict the hourly wage yy of someone with xx years of experience. The correlation is r=0.56r=0.56. Suppose that xx is changed to months of experience.
(a) Which of the following will be the correct regression line?
Linear Regression
Parts (a) to (c) are based on the following information.
The equation y^\hat{y}=35+1.50xx is used to predict the hourly wage yy of someone with xx years of experience. The correlation is r=0.56r=0.56. Suppose that xx is changed to months of experience.
(a) Which of the following will be the correct regression line?
Linear Regression
Parts (a) to (c) are based on the following information.
The equation y^\hat{y}=35+1.50xx is used to predict the hourly wage yy of someone with xx years of experience. The correlation is r=0.56r=0.56. Suppose that xx is changed to months of experience.
(a) Which of the following will be the correct regression line?
Linear Regression
Parts (a) to (c) are based on the following information.
The equation y^\hat{y}=35+1.50xx is used to predict the hourly wage yy of someone with xx years of experience. The correlation is r=0.56r=0.56. Suppose that xx is changed to months of experience.
(a) Which of the following will be the correct regression line?
Linear Regression
Parts (a) to (c) are based on the following information.
The equation y^\hat{y}=35+1.50xx is used to predict the hourly wage yy of someone with xx years of experience. The correlation is r=0.56r=0.56. Suppose that xx is changed to months of experience.
(a) Which of the following will be the correct regression line?
Linear Regression Analysis
The regression model y=4000−2.7xy=4000-2.7x is used to estimate the annual number of
cars going over the speed limit (y) based on the number of police officers on
the road. Estimate the number of cars going over the speed limit when there are
Linear Regression: Residual Plots
The equation y^=35+1.50x\hat{y}=35+1.50x is used to predict the hourly wage yy of someone with xx years of experience.
Elizabeth has 4 years of experience and earns $36 per hour. What is the residual?
Linear regression
Using the information provided below, solve for the slope and intercept of the linear regression equation.
∑x=702∑y=461∑xy=19,871∑x2=31396∑y2=13,449\begin{array}{ll}\sum x=702\\\sum y=461\\\sum xy=19,871\\\sum x^2=31396\\\sum y^2=13,449\end{array}
Linear regression
Savage question!
(iv) What body fat % would you predict for Mike who is turning 32 years old?
∑x=702∑y=461∑xy=19,871∑x2=31396∑y2=13,449\begin{array}{ll}\sum x=702\\\sum y=461\\\sum xy=19,871\\\sum x^2=31396\\\sum y^2=13,449\end{array}
Linear Regression Analysis
Suppose we want to see if there is a relationship between the size of a condo unit in Twin Pines and its selling price. We randomly sampled 32 units:
What is the explanatory variable (X)?
Simple Linear Regression Analysis
Given the information below, find the slope and intercept of the regression line. [Provide at least one decimal place, e.g. 8.2. Include negative sign if negative, e.g. -3.9.]
Linear Regression
Parts (a) to (c) are based on the following information.
The equation y^\hat{y}=35+1.50xx is used to predict the hourly wage yy of someone with xx years of experience. The correlation is r=0.56r=0.56. Suppose that xx is changed to months of experience.
(a) Which of the following will be the correct regression line?
Correlation and Residual Plots
Pamela and Tim are trying to be healthy by taking the stairs instead of the elevator. We want to see there is a relationship between the floor you live on and the time it takes to get to your floor from the lobby. Pamela lives on the 8th floor. Tim lives in the penthouse on the 20th floor.
∑x2=258\sum_{ }^{ }x^2=258
∑y2=687\sum_{ }^{ }y^2=687
Simple Linear Regression: Correlation
True or false? Enter T if True, F if False. (Use capital letters: T, F)
1. If 𝒓 = −𝟏. 𝟎𝟎, then 100% of the data points fall exactly on the regression line and the slope cannot be equal or greater than 0.
T
Simple Linear Regression
Here is a residual plot for selling price (Y) vs. size of a condo in square-feet (X).
Which condition is violated?
More Predictions and Residual Plots Questions:
You wish to predict the number of times someone logs in their email a day. The estimated linear regression model is Logins = 5 + 1.3(work)Logins\ =\ 5\ +\ 1.3\left(work\right)where Logins is the number of times a person logs in their email a week and work is the number of hours of work done a week. Logan works 30 hours a week and checks his email 49 times a week. What is his residual?
Predictions and Residual Plots
You wish to predict the number of times someone logs in their email a day.
The estimated linear regression model is Logins=5+1.3(work)Logins=5+1.3\left(work\right)
where Logins is the number of times a person logs in their email a week and
Predictions and Residual Plots
One day George wanted to examine the linear relationship between how many hours a student studies (xx) the day before the exam and the exam grade (yy).
He asks 13 random students and collects the following data
r=0.819  sx=3.24  sy=14.24  x‾=8.25  y‾=78.25r=0.819\ \ s_x=3.24\ \ s_y=14.24\ \ \overline{x}=8.25\ \ \overline{y}=78.25
Predictions and Residual Plots
Suppose the following summary data was given:
x-variable: μx=10, sx=2\mu_x=10,\ s_x=2
y-variable: μy=250, sy=10\mu_y=250,\ s_y=10
Linear Regression: Residual Plots
The equation y^=35+1.50x\hat{y}=35+1.50x is used to predict the hourly wage yy of someone with xx years of experience.
Elizabeth has 4 years of experience and earns $36 per hour. What is the residual?
Predictions: Residual Plots
In investigating the relationship between number of people on a person's phone list (xx) and the number of text messages per day (yy), 40 individuals were surveyed and the following information was recorded:
∑i=140xi=700sx=3.5\sum_{i=1}^{40}x_i=700 \quad s_x=3.5
∑i=140yi=3200sy=18.9r=0.67\sum_{i=1}^{40}y_i=3200 \quad s_y=18.9 \quad r=0.67
Predictions and Residual Plots
In linear regression, which of the following indicates that we should fit the scatter plot with a line of best fit?
Predictions and Residual Plots
Suppose we want to see if there is a relationship between the size of a condo unit and the selling price of it. We randomly sampled 16 units:
(a) Estimate the linear regression model.
(b) Unit #3 is 1250 sqft, what is his predicted selling price? Compute the residual for this estimate.
Predictions and Residual Plots
Suppose we want to see if there is a relationship between the size of a condo unit and the selling price of it. We randomly sampled 16 units, here are the results and the regression output:
(a) Estimate the linear regression model.
(b) Unit #3 is 1250 sqft, what is his predicted selling price? Compute the residual for this estimate.
Predictions and Residual Plots
In investigating the relationship between number of people on a person's phone list (xx) and the number of text messages per day (yy), 40 individuals were surveyed and the following information was recorded:
∑i=140xi=700sx=3.5\sum_{i=1}^{40}x_i=700 \quad s_x=3.5
∑i=140yi=3200sy=18.9r=0.67\sum_{i=1}^{40}y_i=3200 \quad s_y=18.9 \quad r=0.67
Predictions and Residual Plots
Cindy calculated residuals and find that all of them are negative. Which of the following is true?
Predictions and residual plots
The following assertions about the three residual are True or False
Residual Plot A: No problem – unbiased and homoscedastic (constant variance)
Residual Plot B: Heteroscedasticity – we see a “fan”, indication of non-constant variance across the range of values
Predictions: Residual Plots
In investigating the relationship between number of people on a person's phone list (xx) and the number of text messages per day (yy), 40 individuals were surveyed and the following information was recorded:
∑i=140xi=700   sx=3.5\sum_{i=1}^{40}x_i=700\quad\ \ \ s_x=3.5
∑i=140yi=3200  sy=18.9  r=0.67\sum_{i=1}^{40}y_i=3200\ \ \quad s_y=18.9\quad\ \ r=0.67
Residual Plots: Calculation
In investigating the relationship between number of people on a person's phone list (xx) and the number of text messages per day (yy), 40 individuals were surveyed and the following information was recorded:
∑i=140xi=700sx=3.5\sum_{i=1}^{40}x_i=700 \quad s_x=3.5
∑i=140yi=3200sy=18.9r=0.67\sum_{i=1}^{40}y_i=3200 \quad s_y=18.9 \quad r=0.67
Predictions and Residual Plots
You wish to predict the number of times someone logs in their email a day. The estimated linear regression model is Logins = 5 + 1.3(work)Logins\ =\ 5\ +\ 1.3\left(work\right)where Logins is the number of times a person logs in their email a week and work is the number of hours of work done a week. Logan works 30 hours a week and checks his email 49 times a week. What is his residual?
Predictions and Residual Plots
The regression model y=4000−2.7xy=4000-2.7x is used to estimate the annual number of cars going over the speed limit (y) based on the number of police officers on the road. Estimate the number of cars going over the speed limit when there are 40 police officers on the road.
Predictions and Residual Plots: Regression Diagnostics Question
Let’s take a look at our residual plot in our example:
Regression Diagnostics (check all that applies):
[ ] Outliers
Predictions: Residual Plots
(v) Mike actually has 8% body fat. What is the residual?
Regression line: Predictions and Residual Plots
True or false?
If the sum of the residuals is equal to 0, then the best-fitting regression line goes through all the data points.
Correlation and Residual Plots
Pamela and Tim are trying to be healthy by taking the stairs instead of the elevator. We want to see there is a relationship between the floor you live on and the time it takes to get to your floor from the lobby. Pamela lives on the 8th floor. Tim lives in the penthouse on the 20th floor.
∑x2=258\sum_{ }^{ }x^2=258
∑y2=687\sum_{ }^{ }y^2=687
Residual Plots
Here are three residual plots. Which of the following is true?
Simple Linear Regression
Here is a residual plot for selling price (Y) vs. size of a condo in square-feet (X).
Which condition is violated?