i. The data below shows the maximum temperature ("C) and the number of bowls of soup sold at God's Grace Canteen on eleven randomly selected days last year. | 1|2 3 4 5 6|78 9 | 10|| 11 Temp. (*C) 32 36 22 29 25 18 11 16 8 5 2 Day Bowls Sold 2 2 10 10 20 35 45 50 50 57 63 i. On graph paper, draw a scatter plot of temperature against bowls sold to represent the data using a scale of 1 cm to represent 5 bowls and 2cm to represent 5"C. i. Write down the type of relationship between the variables. il. Calculate the mean number of bowls of soup, x, sold. iv. Find the mean maximum temperature, y of soup recorded. v. Plot and label the point M ( x, y ) on your graph. vi. The line of best fit for the data passes through the point M and the y- intercept (0, 33.2). Draw this line on your graph. vii. Find the equation of the line of best fit. viii. On another day of the year, the temperature was 20°C. Using the equation of the line of best fit, estimate the number of bowls of soup sold on that day. ix. The shop owner says "We sell more bowls of soup the higher the temperature". Does the scatter graph support this statement? Give a reason for your answer.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
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