please help me here with a and b and do i have c and d correct?, i do not know how to start. john put his spare change in jar every night. If he has $11.09, at end of Jan. . 22.27 at the end of feb, 44.35 in April, 75.82 in july, 89.00 in aug. and 114.76.
a. at the end of oct. what does value of correlation coefficient tell you about correlation of data?
b. write the equation of best fitting line
c. average he puts in jar each month. i divided 114.76 by 10 mo, which covered that period average = 11.48 in jar each month
d. will he have enough saved to buy video game console for $140.00 at end of dec.? no, money needed 140.00 - 114.76 = 25.24 needed.
M = 25.24 - 2(aver 11.48) = 2.28 more needed. he needs to save an extra 1.14 each for nov. and dec,
thank you for your help.
c and d look good
The correlation coefficient is a number between 0 and 1. If there is no relationship between the predicted values and the actual values the correlation coefficient is 0 or very low (the predicted values are no better than random numbers). As the strength of the relationship between the predicted values and actual values increases so does the correlation coefficient. A perfect fit gives a coefficient of 1.0. Thus the higher the correlation coefficient the better.
Best fit line: Look up regression to find the formulas for slope and intercept. Plug in your data and you get
y = 11.33x - 0.88
Steve thank you so much. ann
To solve these problems, we can use the given data and apply some basic statistical concepts. Let's break down each question.
a. To determine the correlation coefficient, we need to first plot the data points on a graph. The x-axis represents the months (Jan, Feb, April, July, Aug, Oct), and the y-axis represents the amount of money in the jar.
1. Plot the data points on a graph: (Jan, $11.09), (Feb, $22.27), (April, $44.35), (July, $75.82), (Aug, $89.00), (Oct, ???).
2. Connect the data points with a line.
3. Calculate the correlation coefficient using a statistical software or an online calculator. The correlation coefficient ranges from -1 to 1, where -1 indicates a perfect negative correlation, 1 indicates a perfect positive correlation, and 0 indicates no correlation.
b. To find the equation of the best fitting line, we can use linear regression analysis. Again, plotting the data points on a graph will help us visualize the trend. This line will approximate the relationship between the months and the amount of money in the jar.
1. Plot the data points on a graph: (Jan, $11.09), (Feb, $22.27), (April, $44.35), (July, $75.82), (Aug, $89.00), (Oct, ???).
2. Use linear regression analysis to find the equation of the line that best fits the data. This equation will have the form y = mx + b, where y is the amount of money in the jar and x is the month.
c. To find the average amount he puts in the jar each month, we need to calculate the mean (average) of the given amounts.
1. Add up all the amounts: $11.09 + $22.27 + $44.35 + $75.82 + $89.00 + $114.76.
2. Divide the total sum by the number of months (6 in this case) to calculate the average.
d. To determine if he will have enough saved to buy a video game console, we need to compare his total savings at the end of December with the cost of the console.
1. Subtract the total savings at the end of December ($114.76) from the cost of the console ($140.00) to find out how much more money he needs.
2. Divide the remaining amount needed by the number of months left (2 in this case) to determine the additional amount he needs to save each month.
Remember, these are just the guidance steps. You can apply these calculations in a spreadsheet or use statistical software to simplify the process.