Create a graphical analysis for categorical variables

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Reference no: EM133886639 , Length: word count:2000

Statistics for Decision Making

Assessment - Statistical Case Scenario Analysis

Assessment Description and Instructions Description

Each student must provide a 2000-words to report the findings of their assignment plus a video presentation will be prepared based on the material discussed. Your assignment will be prepared based on the material discussed and presented in weeks 8 to 11 lectures and tutorials. Consequently, the topics may include evaluation of hypotheses testing, Regression analysis, and Time Series analysis. Please use Excel for statistical analysis in this assignment. Relevant Excel statistical output must be properly analysed and interpreted. Get professional assignment help service now!

Assignment Data

Demonstrate your ability to perform statistical analysis of a data set. You will locate your own data set; a great source of data is available at Kaggle Your data must contribute to addressing the research objective/questions and cover the following:

At least two continuous variables for analysis.

At least two grouping variables, each with two distinct categories.

Assignment Questions
Make sure the following questions are covered in the data analysis:

Create a graphical analysis for categorical variables (e.g.. properly type. presence of a garage) and numerical variables (e.g.. total interior space. property age). Comment on the key findings.

For statistical analysis involving a hypothesis test for comparing the average Interior space of properties with and without a garage: Formulate the null and alternative hypotheses. State your statistical decision using the significant value (o) of 5%.

Evaluate the performance of a simple linear regression analysis using property age as the independent variable and sale price as the dependent variable. Describe and explain your process for variable selection. Justify your choices with regression data analysis.

Check the model assumptions for the simple linear regression model and identity common violations.

Evaluate the performance of a multiple linear regression analysis using three numerical variables moony age, interior space. and land size) to predict sale price.

Describe and explain your process for variable selection. Justify your choices with regression data analysis.

Check the model assumptions for the multiple linear regression model and identity common violations.

Compare the performance of the simple linear regression (Model 1) and the multiple linear regression (Model 2). Determine which model fits better.

At the 0.05 level of significance, determine whether property age makes a significant contribution to the simple linear regression model.

At the 0.05 level of significance, determine whether property age, interior space, and land size make a significant contribution to the multiple linear regression model.

State your conclusion in context.

Reference no: EM133886639

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