Put this solution in exel The two numerical variables that we have chosen are the amount of money spent on advertising and the amount of money earned in revenue. The regression equation is: Y = 0.4644X + 8.4171 The slope of the regression line is 0.4644. This means that for every $1 increase in advertising, there is a $0.4644 increase in revenue. We can test whether the slope of the population is different from zero by using a t-test. The t-statistic is 2.5 and the p-value is 0.016. Since the p-value is less than 0.05, we can conclude that the slope is significantly different from zero. The R-squared value is 0.821, which means that 82.1% of the variation in revenue can be explained by advertising. What is the correlation coefficient? [2] Is the correlation coefficient statistically significant? [2] The correlation coefficient is 0.906. This means that there is a strong positive correlation between advertising and revenue. The correlation coefficient is statistically significant because the p-value is less than 0.05. Interpret the correlation coefficient in the context of the data. [1] The correlation coefficient means that for every $1 increase in advertising, there is a $0.906 increase in revenue. What is the linear association between the two variables? [2] The linear association between advertising and revenue is positive, which means that as advertising increases, revenue also increases. Draw conclusions from your analysis. Based on the results of the regression analysis, we can conclude that there is a strong positive correlation between advertising and revenue. This means that for every $1 increase in advertising, there is a $0.906 increase in revenue. The linear association between advertising and revenue is positive, which means that as advertising increases, revenue also increases. The R-squared value is 0.821, which means that 82.1% of the variation in revenue can be explained by advertising. The t-test shows that the slope is significantly different from zero, which means that advertising does have an effect on revenue. In conclusion, we can say that advertising does have an impact on revenue and that companies should continue to invest in advertising in order to increase their revenue. Explanation: The correlation coefficient is 0.906. This means that there is a strong positive correlation between advertising and revenue. The correlation coefficient is statistically significant because the p-value is less than 0.05. The correlation coefficient means that for every $1 increase in advertising, there is a $0.906 increase in revenue. The linear association between advertising and revenue is positive, which means that as advertising increases, revenue also increases. The R-squared value is 0.821, which means that 82.1% of the variation in revenue can be explained by advertising. The t-test shows that the slope is significantly different from zero, which means that advertising does have an effect on revenue. In conclusion, we can say that advertising does have an impact on revenue and that companies should continue to invest in advertising in order to increase their revenue