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Quantitative Reasoning II Project: Calculations and Visuals

Quantitative Reasoning II Project: Calculations and Visuals

In this stage you will do statistical reasoning and mathematical modeling to show central tendency and two variable analyses, including regression with equation and R squared value from the data set you chose in Week 2. The purpose of this assignment is to have you practice creating visuals using your own data. This shows you how you will be working with data in your own careers.The purpose of this assignment is to gain experience creating visuals using the data for the topic you selected in Week 2. Use statistical reasoning and mathematical modeling to show central tendency and two-variable analyses, including regression with equation and R2 value. Watch How to Create Trendlines and Scatterplots in Excel®.Watch Lynda.com® Video: Adding Trendlines to Charts.Watch Lynda.com® Videos: Creating Pie Charts, Histograms, & Box-and-Whisker Plots.Create at least three visuals.
One visual must be a scatter plot with trend line, equation, R2 value, and prediction value.
Two of the remaining required visuals can be of the following format: histogram, box and whisker plot, or pie chart.
Please note that the data set that you chose in Week 2 includes data that will not be needed to create your visuals. Quantitative reasoning requires critical thinking to decide what data is necessary.
Create a Microsoft® Word document that includes your three visuals and the following items:
Title of your project and the scenario you are addressing
Brief description of each visual (15 to 50 words)
Consider including the following for each visual when applicable:
A chart title that is appropriate for the data
A descriptive x-axis label
A descriptive y-axis label
For your xy scatter plot, make at least one prediction using the trend line equation for a date in the future. How confident are you in this prediction? State your prediction and provide justification (50 to 150 words).
If you created a box and whisker plot, describe the central tendency of the values. What does this tell you about the data and about your project?
Calculate the mean of the sample data.
Click to view an example of the visuals expected for this assignment.Your assignment will be graded using the Week 3 Quantitative Reasoning II Project Grading Guide.Please read and UNDERESTAND this project very carefully and address all points in the way that it is asked. Attached you will find the data set for the topic that I chose( topic #2 security and criminal justice). Thank you and good luck!

excel excel

excel excel

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Quantitative Reasoning II Project: Calculations and Visuals

Quantitative Reasoning II Project: Calculations and Visuals

In this stage you will do statistical reasoning and mathematical modeling to show central tendency and two variable analyses, including regression with equation and R squared value from the data set you chose in Week 2. The purpose of this assignment is to have you practice creating visuals using your own data. This shows you how you will be working with data in your own careers.The purpose of this assignment is to gain experience creating visuals using the data for the topic you selected in Week 2. Use statistical reasoning and mathematical modeling to show central tendency and two-variable analyses, including regression with equation and R2 value. Watch How to Create Trendlines and Scatterplots in Excel®.Watch Lynda.com® Video: Adding Trendlines to Charts.Watch Lynda.com® Videos: Creating Pie Charts, Histograms, & Box-and-Whisker Plots.Create at least three visuals.One visual must be a scatter plot with trend line, equation, R2 value, and prediction value.Two of the remaining required visuals can be of the following format: histogram, box and whisker plot, or pie chart.Please note that the data set that you chose in Week 2 includes data that will not be needed to create your visuals. Quantitative reasoning requires critical thinking to decide what data is necessary. Create a Microsoft® Word document that includes your three visuals and the following items:Title of your project and the scenario you are addressingBrief description of each visual (15 to 50 words)Consider including the following for each visual when applicable:A chart title that is appropriate for the dataA descriptive x-axis labelA descriptive y-axis labelFor your xy scatter plot, make at least one prediction using the trend line equation for a date in the future. How confident are you in this prediction? State your prediction and provide justification (50 to 150 words). If you created a box and whisker plot, describe the central tendency of the values. What does this tell you about the data and about your project?Calculate the mean of the sample data.

Multiple Regression Analysis

Multiple Regression Analysis

Details:The purpose of this assignment is to apply multiple regression
concepts, interpret multiple regression analysis models, and justify
business predictions based upon the analysis.For this assignment, you will use the "Strength" dataset. You will
use SPSS to analyze the dataset and address the questions presented.
Findings should be presented in a Word document along with the SPSS
outputs.The compressive strength (Y) of concrete is influenced by the mixing
proportions and by the time that it is allowed to cure, although the
exact relationship between the strength and the components is unknown.
The provided data includes the results of n = 1030 concrete strength
experiments that include the following:Strength (in MPa): The compressive strength of the concrete.Age (in days): The number of days the concrete was allowed to cured.Coarse_Aggregate (in kg/m3): The proportion of coarse aggregate in the mix.Fine_Aggregate (in kg/m3): The proportion of fine aggregate in the mix.Cement (in kg/m3): The proportion of cement in the mix.Slag (in kg/m3): The proportion of furnace slag in the mix.Superplasticizer (in kg/m3): The proportion of plasticizer in the mix.Water (in kg/m3): The proportion of water in the mix.Ash (in kg/m3): The proportion of fly ash in the mix.Part 1:Derive various transformations of compressive strength to determine
which transformation, if any, results in a variable that most closely
mimics a normal distribution. To do this, plot Q-Q plots after each
transformation listed below, and decide which one should be used to
build a multiple linear model. Explain your answer and provide the SPSS
output as an illustration.Strength (no transformation)Square root of StrengthSquared Strength(Natural) Log of StrengthReciprocal of StrengthPart 2:Based on the transformation selected in Part 1, build a multiple linear regression model with all eight predictors.Use t-tests to determine if any of the predictors significantly
affect the compressive strength of concrete. Explain why each variable
should or should not be included in the model. Assume α = 0.05. Show the
appropriate model results to explain your answer.If any predictors from question 1 are found to be not significant,
remove them and re-run the model to create a reduced model (RM). Are all
the remaining variables still statistically significant? Show the
appropriate model results to explain your answer.Based on the RM, should there be concern about multicollinearity
among the predictors selected? Show the appropriate model results to
explain your answer.After fitting the RM, derive the residual plot (standardized
residuals vs. standardized predicted values) and normal probability
plot. Interpret each plot.What is the coefficient of determination, R2, of the RM? How would you interpret the R2?Based on the RM, what would be the new estimated compressive
strength that is currently 50 MPa, after a 10-day increase in curing
time? Assume all other predictors are held constant.How would you interpret the intercept (constant) in the RM? Does the
interpretation make sense given the data you used to build the RM?Part 3:Given the following components and aging time below, what is the estimated compressive strength based on the RM?Age: 50 daysCoarse_Aggregate: 900 kg/m3Fine_Aggregate: 600 kg/m3Cement: 300 kg/m3Slag: 200 kg/m3Superplasticizer: 7 kg/m3Water: 190 kg/m3Ash: 70 kg/m3Part 4:What is a 95% confidence interval of the estimate in Part 3? How would you interpret the 95% confidence interval? (Hint: Use the SPSS scoring wizard to address this question.)APA format is not required, but solid academic writing is expected.This assignment uses a grading rubric. Please review the rubric prior
to beginning the assignment to become familiar with the expectations
for successful completion.

Using Excel to finish the assignment

Using Excel to finish the assignment

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