Regression analysis worksheet

statistics exercise and need support to help me learn.

Requirements: Answers
To get the maximum number of points, you must solve both problems, provide screen captures with detailed information, and any information that demonstrates how you solved each problem. PROBLEM 2 1. Description of the dataset: This dataset is a record of 7 common different fish species in fish market sales. With this dataset, a predictive model can be performed using machine-friendly data, and estimate the weight of fish can be predicted. Variable name Variable meaning Species The species of the fish Weight Weight of the fish in grams Length1 Vertical length in cm Length2 Diagonal length in cm Length3 Cross length in cm Height Height in cm Width Diagonal width in cm Source: https://www.kaggle.com/datasets/aungpyaeap/fish-market Requirements: 1. Given the above dataset, can you explain which variables (except for the Weight) explain the Weight of the fish variable? In other words, can you fit a regression model that explains the Weight of the fish variable? 2. If you fitted a regression model, please write the linear equation of the model, and explain each coefficient. 3. Are the assumptions for the linear regression violated or not? (Show your proofs to support your statement)
4. Given the answers to the previous three requirements, do you think our model can be used to predict the Weight of the fish of a new fish? If yes, in what conditions? If not, why can it not be used? 5. What is the interpretation of the R-squared value? What is the interpretation of the Adjusted R-squared model? 6. Is the entire model significant? If yes, why? What is the p-value? Can you explain what it means for the entire model to be significant? 7. Please provide outputs of your regression model and all the other tests performed related to the linear regression assumptions.

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