Determine if age is correlated to glucose level of six individuals. Use Pearson r correlation coefficient
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Determine if age is correlated to glucose level of six individuals. Use Pearson r
Subject |
Age (x) |
Glucose level (y) |
1 |
33 |
99 |
2 |
28 |
67 |
3 |
25 |
78 |
4 |
43 |
75 |
5 |
57 |
88 |
6 |
59 |
91 |
Ho:
Ha:
Calculated Value:
Tabulated Value:
Decision:
Conclusion:
Step by step
Solved in 2 steps
- nalysis Name: Date: Score: Section Exercise 10.3 1. SJS Company has been selling to retail customers in the Metro Manila area. They advertise extensively on radio, print ads, and in the internet. The owner would like to review the relationship between the amount spent on advertising expense (in P000s) and sales (in P000s). Below is information on advertising expense and sales for the last 9 months. Month Jan Advertising Expense 10 Sales Revenue Feb Mar Apr May June July Aug Sept 8 12 11 13 190 215 190 210 235 208 15 14 13 16 170 175 250 Find the coefficient of correlation. Determine at the 0.10 significance level whether the correlation in the population is greater than zero. Step 1: State the hypotheses. Ho: Hi: Step 2: The level of significance and critical region. a= Step 3: Complete the table and compute for the value of r and t. and tcritical = Month x x 22 x2 xy Jan Feb Mar Apr May Jun Jul Aug Sept Σχ= Σy= ΣΥ Σαν = Σx=- tcomputed= r = Step 4: Decision rule. Step 5: Conclusion.…The following sample contains values of the price and quantity sold of a particular commodity. Use quantity sold as the dependent variable Y. Price (X) 6.00 3.00 7.00 1.00 2.00 8.00 Quantity sold (Y) 53 67 48 76 70 28 ∑x}=27, ∑y=342, ∑x2=163, ∑xy=1295, ∑y2 = 21062, n=6. d. Find the coefficient of determination and interpret .The data below is 12 observations of Math SAT scores (x) and scores on Math placement test (y). Calculate the linear correlation coefficient, r. Enter your answers to two decimal places. Y 580 619 510 476 590 506 550 469 450 523 470 392 500 508 440 462 600 625 530 569 410 346 430 508 Question Help: Message instructor Submit Question Chp Ce %23 7 8. 9. W <6 64
- The data in the table to the right are based on the results of a survey comparing the commute time of adults to their score on a well-being test. Complete parts (a) through (d) below. LOADING... Click the icon to view the table of critical values of the correlation coefficient. a) Which variable is likely the explanatory variable and which is the response variable? Critical Values for Correlation Coefficient n 3 0.997 4 0.950 5 0.878 6 0.811 7 0.754 8 0.707 9 0.666 10 0.632 11 0.602 12 0.576 13 0.553 14 0.532 15 0.514 16 0.497 17 0.482 18 0.468 19 0.456 20 0.444 21 0.433 22 0.423 23 0.413 24 0.404 25 0.396 26 0.388 27 0.381 28 0.374 29 0.367 30 0.361 n (a) Which variable is likely the explanatory variable and which is the response variable? The explanatory variable is…The ages of the runners in a race and their finishing positions are given in the following table. Assuming x is the finish position and y is the person's age, is there a significant correlation between age and finishing position? Answer Choices Not significant Not one of these Significant at both 5% and 1% Significant at 5% but not 1% Significant at 1%Listed below are paired data consisting of amounts spent on advertising (in millions of dollars) and the profits (in millions of dollars). Determine if there is a significant negative linear correlation between advertising cost and profit . Use a significance level of 0.01 and round all values to 4 decimal places. Advertising Cost Profit 17 4 15 20 6. 28 7 17 8 29 Ho: p = 0 На: р < 0 Find the Linear Correlation Coefficient r = Find the p-value p-value = The p-value is Less than (or equal to) a O Greater than a The p-value leads to a decision to Ассept Ho Reject Ho O Do Not Reject Ho The conclusion is O There is a significant negative linear correlation between advertising expense and profit. There is a significant positive linear correlation between advertising expense and profit. O There is a significant linear correlation between advertising expense and profit. O There is insufficient evidence to make a conclusion about the linear correlation between advertising expense and profit.
- Determine if there is a significant correlation between the sets of data at a 10% significance level. X 63 32 28 23 39 37 43 21 y 89 64 61 57 70 68 73 56 Negative Critical Value, tcrit [three decimal accuracy] Positive Critical Value, tcrit [three decimal accuracy] Test Statistic, ttest %3D [three decimal accuracy] Test Conclusion: Reject Ho. There is enough evidence to suggest a significant (positive or negative) linear correlation between the data sets. Fail to Reject Ho. There is not enough evidence to suggest a significant (positive or negative) linear correlation between the data sets.The data below was taken from the fat (g) and sodium (mg) found in different types of food found at fast food restaurants. 19 31 34 35 39 39 43 _y 920 1310 860 1180 940 1260 1500 a) Find the p-value to determine if there is a linear correlation between fat (g) and sodium (mg). Record the p-value below. Round to four decimal places. p-value = b) Is there a linear correlation between fat (g) and sodium (mg)? c) If there is a linear correation, write the correlation coefficient below. Otherwise, leave it blank. Round your final answer to four decimal places. d) If there is a linear correlation, write the regression equation below. Otherwise, leave it blank. Round all numbers to four decimal places. e) Using the data shown above, predict the the sodium found in fast food when the fat is 32 g. Round your final answer to two decimal places. f) If there is a linear correlation, what percentage of variation in sodium (mg) can be explained by fat (g)? If there is not a linear correlation, leave…Listed below are paired data consisting of amounts spent on advertising (in millions of dollars) and the profits (in millions of dollars). Determine if there is a significant linear correlation between advertising cost and profit . Use a significance level of 0.05 and round all values to 4 decimal places. Advertising Cost Profit 3 19 4 16 24 6 29 7 25 27 10 30 Ho: p = 0 На: р * 0 Find the Linear Correlation Coefficient r = Find the p-value p-value = The p-value is O Less than (or equal to) a O Greater than a The p-value leads to a decision to O Do Not Reject Ho O Accept Ho O Reject Ho The conclusion is O There is a significant negative linear correlation between advertising expense and profit. O There is a significant linear correlation between advertising expense and profit. O There is a significant positive linear correlation between advertising expense and profit. O There is insufficient evidence to make a conclusion about the linear correlation between advertising expense and…
- For the accompanying data set, (a) draw a scatter diagram of the data, (b) by hand, compute the correlation coefficient, and (c) determine whether there is a linear relation between x and y. Click here to view the data set. Click here to view the critical values table. Critical values for the correlation coefficient Critical Values for Correlation Coefficient Data set 3 0.997 4 0.950 2 4 6. 6. 7 0.878 0.811 y 4 8. 12 14 19 7 0.754 8 0.707 0.666 10 0.632 11 0.602 12 0.576 13 0.553 14 0.532 15 0.514 16 0.497 17 0.482 18 0.468 19 0.456 20 0.444 21 0.433 22 0.423 23 0.413 24 0.404 25 0.396 26 0.388 27 0.381 28 0.374 29 0.367 30 0.361 Print Done nListed below are paired data consisting of amounts spent on advertising (in millions of dollars) and the profits (in millions of dollars). Determine if there is a significant linear correlation between advertising cost and profit Use a significance level of 0.01 and round all values to 4 decimal places. Advertising Cost Profit 3 21 4 16 5 29 17 7 28 8 21 Ho: p = 0 Ha: p +0 Find the Linear Correlation Coefficient r= Find the p-value p-value = The p-value is Less than (or equal to) a OGreater than a The p-value leads to a decision to Do Not Reject Ho OReject Ho OAccept HoDo all Please calculate: Parameters a and b Correlation coefficient Determination coefficient Write correlation equation Visualise data in Scatter chart Provide explanation of results Year Sales, MEUR (Yi) Employees with higher education (Xi) 2010 70 10 2011 90 12 2012 100 15 2013 80 13 2014 110 17 2015 120 20 2016 150 25 2017 200 25 2018 200 25