StatKey Descriptive Statistics for Two Quantitative VariablesCustom DatasetShow Data TableEdit DataUpload FileChange Column(s)2000Summary Statistics Switch VariablesStatisticmedianhouseholdincome casesper100kMean65057.304652.217Standard150011542.583435.999DeviationSample Size23Correlation-0.115Slope-0.00441000Intercept935.699Scatterplot ControlsShow Regression Line5000.45000500005500060000650007000075000800008500090000medianhouseholdincomecasesper100k 1. Using the scatterplot you constructed, do there appear to be any outliers (if you find thatthere are outliers give their values (x and y coordinates).2. Find the correlation coefficient (give the appropriate symbol and the value)Symbol =value =3. Interpret the correlation coefficient in context of the problem.4. Find the regression equation. Give the slope and the y interceptY intercept,Slope =equation:5.Interpret the Y intercept in context. If you feel y intercept doesn't make sense in this caseexplain why.6. Interpret the slope in context.7. Using the scatterplot find the data point that appears to have the largest positive residual(give the x and y coordinate)X= Y=8. compute the residual for the data point you found in #7.Residual =Show work9. List a possible confounding variable and explain why you believe it is a confounding variablePart 2Using the same set of data, create a bootstrap distribution (with 1000 samples) using thesample as your original bootstrap sample. (note: you will need to upload the data again for thebootstrap distribution)10.What does each object in your bootstrap distribution represent (be specific include theappropriate symbol)11. using the bootstrap distribution, create a 90% confidence intervalinterval is12. What parameter are you estimating? (indicate what the parameter represents in contextand be sure to include the appropriate symbol- see introduction for how to represent symbols13. Interpret the interval in context.14.Using this interval, can you make any conclusions as to whether the value of your parameteris positive, negative or 0). Explain your answer for credit

000 Summary Statistics Switch Variables Statistic medianhouseholdincome casesper100k Мean 65057.304 652.217 Standard 500 11542.583 435.999 Deviation Sample Size 23 Correlation -0.115 Slope -0.0044 000 Intercept 935.699 Scatterplot Controls O Show Regression Line 500

000 Summary Statistics Switch Variables Statistic medianhouseholdincome casesper100k Мean 65057.304 652.217 Standard 500 11542.583 435.999 Deviation Sample Size 23 Correlation -0.115 Slope -0.0044 000 Intercept 935.699 Scatterplot Controls O Show Regression Line 500

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Concept explainers

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

Question

StatKey Descriptive Statistics for Two Quantitative Variables
Custom Dataset
Show Data Table
Edit Data
Upload File
Change Column(s)
2000
Summary Statistics Switch Variables
Statistic
medianhouseholdincome casesper100k
Mean
65057.304
652.217
Standard
1500
11542.583
435.999
Deviation
Sample Size
23
Correlation
-0.115
Slope
-0.0044
1000
Intercept
935.699
Scatterplot Controls
Show Regression Line
500
0.
45000
50000
55000
60000
65000
70000
75000
80000
85000
90000
medianhouseholdincome
casesper100k

1. Using the scatterplot you constructed, do there appear to be any outliers (if you find that
there are outliers give their values (x and y coordinates).
2. Find the correlation coefficient (give the appropriate symbol and the value)
Symbol =
value =
3. Interpret the correlation coefficient in context of the problem.
4. Find the regression equation. Give the slope and the y intercept
Y intercept,
Slope =
equation:
5.Interpret the Y intercept in context. If you feel y intercept doesn't make sense in this case
explain why.
6. Interpret the slope in context.
7. Using the scatterplot find the data point that appears to have the largest positive residual
(give the x and y coordinate)
X= Y=
8. compute the residual for the data point you found in #7.
Residual =
Show work
9. List a possible confounding variable and explain why you believe it is a confounding variable
Part 2
Using the same set of data, create a bootstrap distribution (with 1000 samples) using the
sample as your original bootstrap sample. (note: you will need to upload the data again for the
bootstrap distribution)
10.What does each object in your bootstrap distribution represent (be specific include the
appropriate symbol)
11. using the bootstrap distribution, create a 90% confidence interval
interval is
12. What parameter are you estimating? (indicate what the parameter represents in context
and be sure to include the appropriate symbol- see introduction for how to represent symbols
13. Interpret the interval in context.
14.Using this interval, can you make any conclusions as to whether the value of your parameter
is positive, negative or 0). Explain your answer for credit

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