Vizly: Analysis of Linear Regression Model Fit

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Analysis of Linear Regression Model Fit

tempexam.csv

Missing file

I would like to execute a linear regression model on this data

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The linear regression model has been successfully fitted to the data. Here are the key results:

Intercept: -63.66
Slope (Dates): 0.0295
R-squared: 0.075
Adjusted R-squared: 0.070
Residual standard error: 5.528
F-statistic: 14.77
p-value: 0.000168

The model indicates a positive relationship between the dates and the variable y, with a slope of 0.0295. The p-value is very small, indicating that the relationship is statistically significant.

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default of credit card clients-csv.csv

Missing file

what can you tell me about this data set

The dataset provided is named default of credit card clients. It contains information about credit card clients and whether they defaulted on their payment the next month. Here are some key details about the dataset:

ID: Unique identifier for each client.
LIMIT_BAL: Amount of the given credit (in NT dollars).
SEX: Gender (1 = male, 2 = female).
EDUCATION: Education level (1 = graduate school, 2 = university, 3 = high school, 4 = others).
MARRIAGE: Marital status (1 = married, 2 = single, 3 = others).
AGE: Age of the client.
PAY_0 to PAY_6: History of past payment (from September to April, -1 = pay duly, 1 = payment delay for one month, 2 = payment delay for two months, etc.).
BILL_AMT1 to BILL_AMT6: Amount of bill statement (from September to April).
PAY_AMT1 to PAY_AMT6: Amount of previous payment (from September to April).
default payment next month: Whether the client defaulted on their payment the next month (1 = yes, 0 = no).

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The dataset has been successfully loaded. Here are the first few rows of the dataset:

ID	LIMIT_BAL	SEX	EDUCATION	MARRIAGE	AGE	PAY_0	PAY_2	PAY_3	PAY_4	PAY_5	PAY_6	BILL_AMT1	BILL_AMT2	BILL_AMT3	BILL_AMT4	BILL_AMT5	BILL_AMT6	PAY_AMT1	PAY_AMT2	PAY_AMT3	PAY_AMT4	default payment next month
1	20000	2	2	1	24	2	2	-1	-1	-2	-2	3913	3102	689	0	0	0	0	689	0	0	1
2	120000	2	2	2	26	-1	2	0	0	0	0	3272	3455	3261	0	1000	1000	0	1000	0	2000	1
3	90000	2	2	2	34	0	0	0	0	0	0	14331	14948	15549	1518	1500	1000	1000	1000	5000	0	0
4	50000	2	2	1	37	0	0	0	0	0	0	28314	28959	29547	2000	2019	1200	1100	1069	1000	0	0

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