Complete the following multiplication exercises.
2 answers:
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Answer:
Step-by-step explanation:
Two cases arise to obtain the median.
Case (1): Sample size (n) is odd
First, arrange the data in ascending order. Then, the median is the middle value and lies at the position.
Case (2): Sample size (n) is even
First, arrange the data in ascending order. Then, the median is the mean of the two data values that lie on and positions.
Formula for mean is, where is the sum of all observations and n is the total number of observations.
(1)
Use the below process to create a set of 5 positive numbers (repeats allowed) that have median 7 and mean 10.
1.Select the 5 ordered numbers with middle value 7.
2.Adjust the remaining 4 numbers other than 7 to get mean 10 without changing the order of the numbers.
Based on the process to select the 5 ordered numbers with middle value 7 and change the other ordered numbers to get the mean value 10.
(2)
The product of the mean and the total number of observations is obtained. Then, the sum of four observations is subtracted from the product.
Here, the first four numbers are (5, 6, 7, 8).
The last number by using mean 10 is,
The last number is 24.
Answer:
Daniel can read his data and refer to line as best line of fit and estimate an average per set of hours.
Step-by-step explanation:
A line of fit draws a solid conclusion to the average for the hours spent during the amount of indicated hours. We draw a line of fit central fit and aim similar centrality as that similar results of the mean (without working out the mean we can draw a line perpendicular to the number of mean, but in line of fit we go central to all the descending or cascading results to include all results but just using one line), with one further consideration and that is balance if anything sticks out from the norm ie) weather conditions including data, we suggest if there is nothing to weigh the line of fit to a balancing outcome that shows the opposite of kilometres walked (eg. extreme higher mileage within the hour/s) then it may just alter the line a fraction of how many treks he did, but not in data less than 30 entries. Have attached an example where they classify in economics something outside the norm is called a misfit. Daniel can read his data and refer to line as best line of fit and estimate an average per set of hours. Here on the attachment you can read any misfit info and use the line coordination perpendicular to guide the indifference, the attachment shows it is not really included in the best line of fit as other dominating balances have occurred and therefore we have a misfit, all whilst using best line of fit to balance everything fairly.
