Linear Regression and K$-$means Clustering

Instructions:

For each question, students should:

$$ Use R scripts to perform the analysis.

$$ Generate necessary plots and include them in the final document.

$$ Provide detailed explanations for their observations and interpretations.

$$ Compile all scripts, plots, results, and explanations in a Word or PDF document and

submit it$.$

Question $1$: Multiple Regression Analysis $(15$ points$)$

Using the dailyactivity$\_$merged dataset, perform a multiple regression analysis where the

response variable is calories burned $($Calories$)$ and the predictor variables are:

$$ Sedentary minutes $($sedentaryMinutes$)$

$$ Very active minutes $($veryActiveMinutes$)$

$$ Lightly active minutes $($lightlyActiveMinutes$)$

Tasks:

$1.$ Load the dataset and perform a multiple regression analysis.

$2.$ Create diagnostic plots to check assumptions.

$3.$ Interpret the summary statistics of your regression model.

Deliverables:

$$ R scripts used for analysis. $(5$ points$)$

$$ Plots generated during the analysis. $(3$ points$)$

$$ Written interpretation of the results, including any assumptions checked and their

implications. $(7$ points$)$

Question $2$: K$-$means Clustering $(15$ points$)$

Using the hourlyintensities$\_$merged dataset, perform k$-$means clustering using the following

two features:

$$ Total intensity $($totalintensity$)$

$$ Average intensity $($averageintensity$)$

Tasks:

$1.$ Load the dataset and perform k$-$means clustering. Determine the optimal number of

clusters, and explain why you chose your optimal number.

$2.$ Visualize the clusters using a scatter plot.

$3.$ Interpret the clusters formed and discuss any patterns or insights you observe regarding

activity intensity.

Deliverables:

$$ R scripts used for analysis. $(5$ points$)$

$$ Plots generated during the analysis. $(3$ points$)$

$$ Written interpretation of the clustering results, including any patterns identified. $(7$

points$)$

Linear Regression and K$-$means Clustering

Instructions:

For each question, students should:

Use R scripts to perform the analysis.

Generate necessary plots and include them in the final document.

Provide detailed explanations for their observations and interpretations.

Compile all scripts, plots, results, and explanations in a Word or PDF document and

submit it$.$

Question $1$: Multiple Regression Analysis $(15$ points$)$

Using the dailyactivity$\_$merged dataset, perform a multiple regression analysis where the

response variable is calories burned $($Calories$)$ and the predictor variables are:

Sedentary minutes $($sedentaryMinutes$)$

Very active minutes $($veryActiveMinutes$)$

Lightly active minutes $($lightlyActiveMinutes$)$

Tasks:

Load the dataset and perform a multiple regression analysis.

Create diagnostic plots to check assumptions.

Interpret the summary statistics of your regression model.

Deliverables:

R scripts used for analysis. $(5$ points$)$

Plots generated during the analysis. $(3$ points$)$

Written interpretation of the results, including any assumptions checked and their

implications. $(7$ points$)$

Question $2$: K$-$means Clustering $(15$ points$)$

Using the hourlyintensities$\_$merged dataset, perform k$-$means clustering using the following

two features:

Total intensity $($totalintensity$)$

Average intensity $($averageintensity$)$

Tasks:

Load the dataset and perform k$-$means clustering. Determine the optimal number of

clusters, and explain why you chose your optimal number.

Visualize the clusters using a scatter plot.

Interpret the clusters formed and discuss any patterns or insights you observe regarding

activity intensity.

Deliverables:

R scripts used for analysis. $(5$ points$)$

Plots generated during the analysis. $(3$ points$)$

Written interpretation of the clustering results, including any patterns identified. $(7$

points$)$ please write the code in R program R script. No python