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Choice of statistical analysis is based largely on the way in which the variables have been measured. Consider the following variable and identify if it is more likely to be measured on a metric or categorical scale:
variable: was the carriage/tram/bus overcrowded. (Yes/No)
is the answer metric or categorical?

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Related textbook solutions

Introductory Statistics
OER Edition, 2013
Openstax
ISBN: 9781938168208

Related questions

Inalize three research questions relating to your selected dataset. Make sure the variables used in the final questions you select are of the following format:
1. Continuous response and continuous explanatory variable
2. Categorical response and categorical explanatory variable
3. Continuous response and categorical explanatory variable (note: the categorical variable must be dichotomous for this question)
Each research question should have a clear explanatory and response variable. Review, as necessary, the ‘Laerd Statistics’ reading from last week which explains data types and the difference between response variables (also referred to as dependent or outcome variables) and explanatory variables (also referred to as independent or predictor variables).
Then, in your initial post, list your three finalized research questions, indicating the variable types.
For example, using data from the Framingham Heart Study dataset, one question might be: ‘Is there a significant difference in total serum cholesterol levels [TOTCHOL] (continuous-ratio) (response) between smokers and non-smokers [CURSMOKE] (categorical-dichotomous) (explanatory)?’
In your response post to your peers, provide feedback on their finalized research questions, which will become the foundation of each student’s final project. Consider the following questions as you review your peers' research questions:
• Is each research question clearly stated?
• Does each research question relate to the dataset?
• Do the three research questions meet the data type requirements?
• Does each research question identify response and explanatory variables correctly?

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?

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.

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