Question

In: Statistics and Probability

Statistics - Calculate difference in proportions and chi-square values. Do by hand, not in R or...

Statistics - Calculate difference in proportions and chi-square values.

Do by hand, not in R or Excel. Show formulas

1) Calculate difference in proportions

2) Calculate chi-square values

Cell phones and cancer

Cancer Cancer
Cell phone Yes No
Yes 53 102
No 485 693

Solutions

Expert Solution

1) H0: There is no significance difference among the proportion of cell phone users and attack cancer

H1: There is significance difference among the proportion of cell phone users and attack cancer

Let the los be alpha = 5%

From the given data

Cancer
Cell Phone Yes NO Total
Yes 53 102 155
NO 485 693 1178
Total 538 795 1333

1st sample proportion p1 = 53/155 = 0.341935
2nd sample proportion p2 = 485/1178 = 0.411715

P-value = 0.0960

Z-critical value = +/- 1.96

Here P-value > alpha 0.05 and z value is in z critical values, so we accept H0

Thus we conclude that There is no significance difference among the proportion of cell phone users and attack cancer

2)

H0: There is no significance difference among the proportion of cell phone users and attack cancer

H1: There is significance difference among the proportion of cell phone users and attack cancer

Let the los be alpha = 5%

Cancer
Cell Phone Yes NO Total
Yes 53 102 155
NO 485 693 1178
Total 538 795 1333

Critical X^2: 3.841456
P-Value: 0.0960

Here P-value > alpha 0.05 and Chisquare value < Chisquare critical value, so we accept H0

Thus we conclude that There is no significance difference among the proportion of cell phone users and attack cancer


Related Solutions

Using the Chi-Square test for the difference between two proportions, determine (?=0.05) if there is a...
Using the Chi-Square test for the difference between two proportions, determine (?=0.05) if there is a difference in population proportion between male patients using Medicare as their Payer versus female patients using Medicare as their Payer. Be sure to set up the null and alternate hypotheses.   Using the Patients dataset, determine if there is a relationship between length of stay and total charges. Your answer should include the following: Scatter Plot   Linear Regression Equation (with interpretation)   Coefficient of Determination (with...
1) What’s the difference among the chi-square test for goodness of fit, the chi-square test for...
1) What’s the difference among the chi-square test for goodness of fit, the chi-square test for independence, and the chi-square test for homogeneity 2) State the requirements to perform a chi-square test
what is the difference between chi square and z test?
what is the difference between chi square and z test?
The topic for this week is Inferential Statistics including the Chi-Square, Correlation and Regression as well...
The topic for this week is Inferential Statistics including the Chi-Square, Correlation and Regression as well as Time Series Analysis.  Correlation and Regression are used in basic modeling where we forecast the value of a dependent variable based on the value of an independent variable.   You may want to review the lecture entitled Correlation and Regression posted in this week’s discussion stream.   Be sure to use the Excel charting to construct a Scatterplot of two of the variables shown in the attached...
In chi-square statistics - how are you able to determine multiple degrees of freedoms?
In chi-square statistics - how are you able to determine multiple degrees of freedoms?
Using Excel: Regression Statistics Multiple R 0.9021 R- Square 0.8138 Adjusted R Square 0.7828 Standard Error...
Using Excel: Regression Statistics Multiple R 0.9021 R- Square 0.8138 Adjusted R Square 0.7828 Standard Error 9.4006 ANOVA df SS MS F Regression 1 2317.6 2317.6 26.226 Residual 6 530.23 88.372 Total 7 2847.9 Coefficients Standard Error t Stat P-value Intercept 45.897 5.5447 8.2776 0.0002 Number of Surgeries (x) 5.1951 1.0144 5.1211 0.0022 1. r = 0.90 strong positive correlation 2. y = 5.195 x + 45.897 , 3. r2 = 0.8138 , and 4. Se =  9.4006 5. Results of...
Dep.= Mileage Indep.= Octane SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard...
Dep.= Mileage Indep.= Octane SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 7.0000 ANOVA Significance df SS MS F F Regression 9.1970 Residual Total 169.4286 Standard Coefficients Error t Stat P-value Lower 95% Upper 95% Intercept -115.6768 Octane 1.5305 SE CI CI PI PI Predicted Predicted Lower Upper Lower Upper x0 Value Value 95% 95% 95% 95% 89.0000 1.4274 87.0000 2.0544 Is there a relationship between a car's gas MILEAGE (in miles/gallon) and the...
Linear Regression Regression Statistics R 0.99798 R Square 0.99597 Adjusted R Square 0.99445 Standard Error 1.34247...
Linear Regression Regression Statistics R 0.99798 R Square 0.99597 Adjusted R Square 0.99445 Standard Error 1.34247 Total Number Of Cases 12 Hamb Consump = 176.2709 - 106.6901 * Hamb Price + 4.5651 * Income (1,000s) - 12.1556 * Hot Dog Price ANOVA d.f. SS MS F p-level Regression 3. 3,560.58212 1,186.86071 658.549258 0. Residual 8. 14.41788 1.80224 Total 11. 3,575. Coefficients Standard Error LCL UCL t Stat p-level H0 (5%) rejected? Intercept 176.27093 45.28994 71.83215 280.709717 3.89206 0.0046 Yes Hamb...
SUMMARY OUTPUT Regression Statistics Multiple R 0.727076179 R Square 0.528639771 Adjusted R Square 0.525504337 Standard Error...
SUMMARY OUTPUT Regression Statistics Multiple R 0.727076179 R Square 0.528639771 Adjusted R Square 0.525504337 Standard Error 3.573206748 Observations 455 ANOVA df SS MS F Significance F Regression 3 6458.025113 2152.67504 168.601791 2.7119E-73 Residual 451 5758.280717 12.7678065 Total 454 12216.30583 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 99.0% Upper 99.0% Intercept -0.250148858 0.359211364 -0.6963835 0.48654745 -0.9560846 0.45578693 -1.1793476 0.67904987 RBUK 0.025079378 0.023812698 1.05319345 0.29281626 -0.0217182 0.07187699 -0.0365187 0.08667745 RSUS 0.713727515 0.042328316 16.8617037 8.0578E-50 0.6305423 0.79691273 0.60423372 0.82322131...
SUMMARY OUTPUT Regression Statistics Multiple R 0.72707618 R Square 0.52863977 Adjusted R Square 0.52550434 Standard Error...
SUMMARY OUTPUT Regression Statistics Multiple R 0.72707618 R Square 0.52863977 Adjusted R Square 0.52550434 Standard Error 3.57320675 Observations 455 ANOVA df SS MS F Significance F Regression 3 6458.02511 2152.67504 168.601791 2.7119E-73 Residual 451 5758.28072 12.7678065 Total 454 12216.3058 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 99.0% Upper 99.0% Intercept -0.2501489 0.35921136 -0.6963835 0.48654745 -0.9560846 0.45578693 -1.1793476 0.67904987 RUK 0.02507938 0.0238127 1.05319345 0.29281626 -0.0217182 0.07187699 -0.0365187 0.08667745 RSUS 0.71372752 0.04232832 16.8617037 8.0578E-50 0.6305423 0.79691273 0.60423372 0.82322131...
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT