Data Analysis

Which of the following best describes the role of visual trend identification in data analysis?

• It replaces the need for statistical methods when analyzing data

• It is the final step in confirming trends and making predictions

• It is an exploratory method that should be supported by statistical analysis

• It is used only when regression models are unavailable

What is the most accurate description of expected values in statistical analysis?

• They are used only for calculating standard deviation in datasets

• They represent the minimum value that a dataset can produce

• They reflect the mean or average outcome of a random variable and can be estimated using tools like Excel or regression models

• They are determined solely through visual analysis of data trends

What does a p-value less than 0.05 typically indicate in statistical analysis?

• The null hypothesis is definitely true

• There is likely a statistically significant relationship between the variables

• The data was collected incorrectly

• The variables are not related in any way

Which of the following best describes the purpose of a t-test in statistical analysis?

• To determine if a data point is an outlier in a dataset

• To compare variances between multiple variables

• To assess whether there is a significant difference between the means of two groups

• To predict future values based on a linear trend

What is the primary purpose of linear regression in statistical analysis?

• To classify data into distinct categories

• To model the relationship between variables assuming a linear connection

• To measure the frequency of categorical variables

• To calculate the median value of a dataset

Which of the following statements best explains the role of R-squared in a regression analysis?

• It measures the standard deviation of each independent variable

• It indicates whether the residuals are normally distributed

• It shows the proportion of variance in the dependent variable explained by the model

• It tests if the model’s coefficients are all equal to zero

What does the Mean Squared Error (MSE) represent in predictive modeling?

• The average absolute error between predicted and actual values

• The average of squared differences between actual and predicted values

• The percentage of correct predictions made by the model

• The number of variables included in a regression model