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work out the following formula for the simple linear regression from the corresponding formula given in matrix terms

a) the normal equation

b)Bo ( the B has a hat on the top, but I cannot type it)

c) B1 ( the B has a hat on the top, but I cannot type it)

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Related textbook solutions

Introductory Statistics
OER Edition, 2013
Openstax
ISBN: 9781938168208

Related questions

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 ā€œpriceā€, standard deviation for ā€œmileageā€, skewness for ā€œcylinderā€ and mean for ā€œsoundā€? Test if ā€œpriceā€ 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? Whatā€™s the pearson correlation coefficient between price & mileage and between ā€œcylinderā€ & ā€œliterā€ 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?

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
.
a)
Using R
, compute the change in systolic pressure by subtracting systolic pressure when
relaxed from systolic pressure during the exam; call this variable
X
.
b)
Using R
, repeat part a) for diastolic pressure. Call the resulting variable
Y
.
c)
Using R
, make a scatterplot of
Y
versus
X
, 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
Y
as your response variable and label horizontal and vertical axes.
d) What is the fitted regression model? Provide slope and y-intercept
explicitly
.
Use R
to find them.
e)
Using R
, 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
R
, 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
and y-intercept
explicitly
.
Use R
to find them.

Hypothesis testing with ANOVA opinions about whether caffeine enhances test performance differ. You design a study to test the impact of drinks with different caffeine contents on students' test-taking abilities. You choose 21 students at random from your introductory psychology course to participate in your study. You randomly assign each student to one of three drinks, each with a different caffeine concentration, such that there are seven students assigned to each drink. You then give each of them a plain capsule containing the precise quantity of caffeine that would be consumed in their designated drink and have them take an arithmetic test 15 minutes later.

The students receive the following arithmetic test scores:

Ā  Cola Black Tea Coffee Ā 
Caffeine Content (mg/oz) 2.9 5.9 13.4 Ā 
Ā  85 85 92 āˆ‘X2 = 147,641
Ā  86 89 87 G = 1,755
Ā  82 82 80 N = 21
Ā  75 75 89 k = 3
Ā  66 88 96 Ā 
Ā  78 76 83 Ā 
Ā  87 82 92 Ā 
Ā  T1 = 559 T2 = 559 T3 = 559 Ā 
Ā  SS1 = 338.86 SS2 = 338.86 SS3 = 338.86 Ā 
Ā  n1 = 7 n2 = 7 n3 = 7 Ā 
Ā  M1 = 79.8571 M2 = 79.8571 M3 = 79.8571 Ā 

1.) You plan to use an ANOVA to test the impact of drinks with different caffeine contents on students' test-taking abilities. What is the null hypothesis?

(a) The population mean test score for the cola population is different from the population mean test score for the black tea population.

(b) The population mean test scores for all three treatments are equal.

(c) The population mean test scores for all three treatments are different.

(d) The population mean test scores for all three treatments are not all equal.

Ā 

2.) Calculate the degrees of freedom and the variances for the following ANOVA table:

Source SS df MS
Between - - -
Within 702.28 - -
Total 973.14 - Ā 

Ā 

The formula for the F-ratio is:

Ā 

Ā 

3.) Using words, the formula of the F-ratio can be written as:

Ā 

Ā 

4.) Using the data from the ANOVA table given, the F-ratio can be written as:

Ā 

5.) Calculate for F-ratio:

Ā 

Ā 

6.)Ā At the level of significance, what is your conclusion?

(a)You can reject the null hypothesis; you do not have enough evidence to say that caffeine affects test performance.

(b)You cannot reject the null hypothesis; caffeine does appear to affect test performance.

(c)You cannot reject the null hypothesis; you do not have enough evidence to say that caffeine affects test performance.

(d)You can reject the null hypothesis; caffeine does appear to affect test performance.

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