Statsmodels

Step 1: Load historical sales data and marketing spend data into a Pandas DataFrame.

Step 2: Define a linear regression model using `statsmodels.formula.api.ols()` with sales as the dependent variable and marketing spend as independent variables.

Step 3: Fit the model to the data using `.fit()` method.

Step 4: Use the fitted model to predict future sales based on projected marketing spend.

Step 1: Load historical stock price data into a Pandas DataFrame.

Step 2: Decompose the time series into trend, seasonality, and residual components using `statsmodels.tsa.seasonal_decompose()`.

Step 3: Fit an ARIMA model to the stock price data using `statsmodels.tsa.arima.model.ARIMA()`.

Step 4: Forecast future stock prices using the fitted ARIMA model.

Step 1: Collect data on patient outcomes for both the drug and placebo groups.

Step 2: Perform an independent samples t-test using `statsmodels.stats.weightstats.ttest_ind()` to compare the means of the two groups.

Step 3: Calculate the p-value associated with the t-test.

Step 4: Determine if the p-value is less than the significance level (e.g., 0.05) to reject the null hypothesis and conclude that the drug has a significant effect.

Step 1: Collect data on customer satisfaction scores and various influencing factors (e.g., product quality, customer service, price).

Step 2: Perform a multiple linear regression using `statsmodels.formula.api.ols()` with customer satisfaction as the dependent variable and the influencing factors as independent variables.

Step 3: Analyze the regression coefficients to determine the relative importance of each factor in influencing customer satisfaction.

Step 4: Focus on improving the factors with the largest positive impact on customer satisfaction.

Step 1: Collect data on conversion rates for both versions (A and B) being tested.

Step 2: Perform a proportion test using `statsmodels.stats.proportion.proportions_ztest()` to compare the conversion rates of the two versions.

Step 3: Calculate the p-value associated with the z-test.

Step 4: If the p-value is less than the significance level, conclude that there is a statistically significant difference in conversion rates between the two versions.