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Linear Regressions
Choose any Excel Discussion dataset. Include the name of the dataset. From that dataset, select any two quantitative varia ...
Linear Regressions
Choose any Excel Discussion dataset. Include the name of the dataset. From that dataset, select any two quantitative variables that you suspect will be related (such as age and height for example). What is the name of the dataset you have chosen? Which two variables did you choose?Next, using Excel, calculate the relationship (r value) between the two variables. Recall that the Excel “formula” for correlation is “=CORREL.” What is the r value for the two variables that you have chosen? Is it positive or negative? Is it strong, medium, or weak? Note that it is best to have an r value that is medium or strong. It is recommended that you try a few different variables until you find two variables with an r value between .5 and 1 (or between -.5 and -1).Next, use Excel to create a scatterplot for the two variables. You decide which variable will be dependent (y) and which will be independent (x). On the scatterplot, include the “trendline” and the “equation for the line” using Excel options. Attach your scatterplot to your post.Finally, using the equation of the line that you generated above, plug in any reasonable value for x (your chosen independent variable) and solve the equation for y (your chosen dependent variable). It is up to you to determine which of your two variables is x and which is y. What prediction do you get? Show all your work. In other words, type out the equation, plug in a value for x, and show your solution for y.
6 pages
Stat 3001 Week 3 Project
Instructions: To complete this project, you will need the following materials: STAT DISK User Manual (found in the classro ...
Stat 3001 Week 3 Project
Instructions: To complete this project, you will need the following materials: STAT DISK User Manual (found in the classroom in Doc Sharing)
MAT 534 Keller Graduate School Exploratory Data Analysis Paper
Open the files for the course project and the data set.For each of the five variables, process, organize, present and
su ...
MAT 534 Keller Graduate School Exploratory Data Analysis Paper
Open the files for the course project and the data set.For each of the five variables, process, organize, present and
summarize the data. Analyze each variable by itself using graphical and
numerical techniques of summarization. Use Excel as much as possible,
explaining what the results reveal. Some of the following graphs may be
helpful: stem-leaf diagram, frequency/relative frequency table,
histogram, boxplot, dotplot, pie chart, bar graph. Caution: not all of
these are appropriate for each of these variables, nor are they all
necessary. More is not necessarily better. In addition be sure to find
the appropriate measures of central tendency, the measures of
dispersion, and the shapes of the distributions (for the quantitative
variables) for the above data. Where appropriate, use the five number
summary (the Min, Q1, Median, Q3, Max). Once again, use Excel as
appropriate, and explain what the results mean.Analyze the connections or relationships between the variables.
There are ten possible pairings of two variables. Use graphical as well
as numerical summary measures. Explain the results of the analysis. Be
sure to consider all 10 pairings. Some variables show clear
relationships, while others do not.
5 pages
Ptw Roller Coaster Design
You are on a team of architects. You are charged with building a scale-model replica of one section of a new roller coaste ...
Ptw Roller Coaster Design
You are on a team of architects. You are charged with building a scale-model replica of one section of a new roller coaster before construction gets ...
4 pages
Sampling Distribution
A computer scanner can read a bar code on a package correctly 97% of the time. One company monitors the accuracy of the sc ...
Sampling Distribution
A computer scanner can read a bar code on a package correctly 97% of the time. One company monitors the accuracy of the scanner by randomly sampling ...
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Most Popular Content
Linear Regressions
Choose any Excel Discussion dataset. Include the name of the dataset. From that dataset, select any two quantitative varia ...
Linear Regressions
Choose any Excel Discussion dataset. Include the name of the dataset. From that dataset, select any two quantitative variables that you suspect will be related (such as age and height for example). What is the name of the dataset you have chosen? Which two variables did you choose?Next, using Excel, calculate the relationship (r value) between the two variables. Recall that the Excel “formula” for correlation is “=CORREL.” What is the r value for the two variables that you have chosen? Is it positive or negative? Is it strong, medium, or weak? Note that it is best to have an r value that is medium or strong. It is recommended that you try a few different variables until you find two variables with an r value between .5 and 1 (or between -.5 and -1).Next, use Excel to create a scatterplot for the two variables. You decide which variable will be dependent (y) and which will be independent (x). On the scatterplot, include the “trendline” and the “equation for the line” using Excel options. Attach your scatterplot to your post.Finally, using the equation of the line that you generated above, plug in any reasonable value for x (your chosen independent variable) and solve the equation for y (your chosen dependent variable). It is up to you to determine which of your two variables is x and which is y. What prediction do you get? Show all your work. In other words, type out the equation, plug in a value for x, and show your solution for y.
6 pages
Stat 3001 Week 3 Project
Instructions: To complete this project, you will need the following materials: STAT DISK User Manual (found in the classro ...
Stat 3001 Week 3 Project
Instructions: To complete this project, you will need the following materials: STAT DISK User Manual (found in the classroom in Doc Sharing)
MAT 534 Keller Graduate School Exploratory Data Analysis Paper
Open the files for the course project and the data set.For each of the five variables, process, organize, present and
su ...
MAT 534 Keller Graduate School Exploratory Data Analysis Paper
Open the files for the course project and the data set.For each of the five variables, process, organize, present and
summarize the data. Analyze each variable by itself using graphical and
numerical techniques of summarization. Use Excel as much as possible,
explaining what the results reveal. Some of the following graphs may be
helpful: stem-leaf diagram, frequency/relative frequency table,
histogram, boxplot, dotplot, pie chart, bar graph. Caution: not all of
these are appropriate for each of these variables, nor are they all
necessary. More is not necessarily better. In addition be sure to find
the appropriate measures of central tendency, the measures of
dispersion, and the shapes of the distributions (for the quantitative
variables) for the above data. Where appropriate, use the five number
summary (the Min, Q1, Median, Q3, Max). Once again, use Excel as
appropriate, and explain what the results mean.Analyze the connections or relationships between the variables.
There are ten possible pairings of two variables. Use graphical as well
as numerical summary measures. Explain the results of the analysis. Be
sure to consider all 10 pairings. Some variables show clear
relationships, while others do not.
5 pages
Ptw Roller Coaster Design
You are on a team of architects. You are charged with building a scale-model replica of one section of a new roller coaste ...
Ptw Roller Coaster Design
You are on a team of architects. You are charged with building a scale-model replica of one section of a new roller coaster before construction gets ...
4 pages
Sampling Distribution
A computer scanner can read a bar code on a package correctly 97% of the time. One company monitors the accuracy of the sc ...
Sampling Distribution
A computer scanner can read a bar code on a package correctly 97% of the time. One company monitors the accuracy of the scanner by randomly sampling ...
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