STATS 7 Lecture Notes - Lecture 6: Dependent And Independent Variables, Coefficient Of Determination

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scarlettiger181

1 Feb 2018

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0. 01796 because that is the number attached to the x variable, as given by the data. For every 1 beer drank, we expect the bac to go up about . 01796: r value is close to 1 so it is a strong correlation. It should result in a screen full of data: create the equation: y=a-bx, or severity=1. 83-0. 3253x. The r coefficient is . 97, telling us there is a strong correlation. Slide 7: the r square value tells you what fraction of the variation in y can be explained by the model, and. In the graph, 79% of variation of y can be accounted for by this graph. Slide 8: the answer to the multiple choice question is a, by looking at the graph and estimating. The se(cid:272)o(cid:374)d questio(cid:374) is (cid:374)o(cid:374)se(cid:374)se, (cid:271)e(cid:272)ause a (cid:374)egati(cid:448)e (cid:448)alue is (cid:374)ot o(cid:374)ly i(cid:373)possi(cid:271)le, a(cid:374)d also (cid:449)e do(cid:374) t have data for 200,000 so we cannot predict.

Related Questions

Researchers measured the blood pressures of 22 students in

two situations: when the students were relaxed and when the students were taking an

important examination. The file

systolic.txt

lists the systolic and diastolic pressures for

each student in each situation. Dataset is available at

systolic_url = "https://mcs.utm.utoronto.ca/~nosedal/data/systolic.txt"

Use R

to answer each of the following questions. You have to show

all your work

to get

full credit.

Answers, even if correct, with no justifications will not receive any

marks

Using R

, compute the change in systolic pressure by subtracting systolic pressure when

relaxed from systolic pressure during the exam; call this variable

Using R

, repeat part a) for diastolic pressure. Call the resulting variable

Using R

, make a scatterplot of

versus

, fit a regression line to the data and add

this line to your plot. Use: 2 c) your last name as the main title for your plot. (In my

case it would be: 2 c) Nosedal).

Important note. Use

as your response variable and label horizontal and vertical axes.

d) What is the fitted regression model? Provide slope and y-intercept

, make a residual plot from the regression in part c). Does this plot call into

question the use of a linear model? Why?

Note. Use:

2 e) your last name

as the main title for your plot. (In my case it would

be:

2 e) Nosedal

f) Note the outlier in the residual plot (and on the scatterplot from part c)).

Using

, delete the outlier from the data set. Then repeat parts c) and e). Note. Use:

2 f )

your last name

as the main title for your plots. (In my case it would be:

2 f ) Nosedal

g) What is the fitted regression model (after the outlier has been removed)? Provide slope

2. In this section you will be shown several classes. Inside the code for each class will be a question. You should provide your answer to the question inside the chart. Many of the questions will ask you whether a variable has changed or not. In cases that the variable being asked about is a primitive type, you should provide your answer based on the data stored in the primitive type. In cases that the variable being asked about is a reference type, you should provide your answer based on the data stored at the Object linked to by the reference type. For example, in the first snippet, the question asks about the variable x defined in the main method. Since x is a primitive type, you should answer YES if you think the variable x has had its value changed after the method methodOne() returns. All your answers should go in the chart below: (9 points) Part Answer to question x changed (circle one)? YES NO y changed (circle one)? a changed (circle one)? b changed (circle one)? d1 a changed (circle one)? b changed (circle one)? YES e words changed (circle one)? YES NO Values in array of array referred to by y: What is printed to the screen? (a) public class ReferenceTesti public static void methodOne (int x){ x++; public static void main(String[args) { int x = 0; methodOne (x); //IS THE DATA STORED OR REFERRED TO BY X THE SAME? (ANSWER IN CHART)

Opeele Kyinkafa sells different used 2005 GM cars in excellent condition. The value or the retail price (Price) of a car is dependent on a variety of characteristics such as number of miles the car has been driven (Mileage), manufacturer of the car (Make) such as Buick, Cadillac, Chevrolet, Pontiac, SAAB, and Saturn. The car price is also influenced by the body type (Type) such as sedan, convertible, coupe, hatchback and wagon. It is also affected by number of cylinders (Cylinders) in the engine, the engine size (Liter), cruise control which is an indicator variable representing whether the car has cruise control (yes) or no otherwise and Sound indicator representing whether the car has upgraded speakers (yes) or no otherwise. All cars in this data set were less than one year old when priced and considered to be in excellent condition. Determine which major variables are dummies and are dynamic? Identify 3 potential unobserved heterogenous factors in this dataset. Display the full descriptive statistics of the data in R. Why do you think some variables are starred (*)? What is the median for “price”, standard deviation for “mileage”, skewness for “cylinder” and mean for “sound”? Test if “price” is normally distributed using histogram, kernel density plot and the shapiro wilk test. Create a red scatterplot matrix of price, mileage, cylinder, liter, make. From your graph, is the correlation between price & mileage positive or negative? What’s the pearson correlation coefficient between price & mileage and between “cylinder” & “liter” correct to 2 d.p Create a correlation matrix of the dataset by finding the apt R command for converting all string/dummy variables From the matrix, identify 2 potentially good predictors? Identify 3 pairs of variables that are mostly multicollinear. Which 2 pairs of variables are least correlated? Identify 3 pairs of variables most highly correlated with Cylinder Generate a simple regression plot of price on mileage with a re fitted line. Run the whole multiple regression model (label the equation as "regprice") & use it to answer the other questions. Specify the regression model for the whole data keeping the exact names of the variables. Write the estimated regression equation. In one sentence, which variables are significant and which are not? Justify Is the regression model justifiable? Explain Interpret the coefficient of Mileage, MakeCadillac, TypeSedan, Cylinder, & Cruiseyes. Compare the price of a car with 130,000 mileage, 6 cylinders, 3.8 liters, with cruise and sound, was manufactured by Pontiac with a convertible body type with that of the price of a car with four cylinders, 120,000 mileage, 3.8 liters, without cruise nor sound, was manufactured by Cadillac with a hatchback body type. Which factor(s) appears to be causing a comparative difference (if any)? Using only the VIF, which one of the pairs of variables selected to be multicollinear may be deleted? Justify?

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STATS 7 Lecture Notes - Lecture 6: Dependent And Independent Variables, Coefficient Of Determination

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