Written Report Adam Hassen, Matt Herrig, David Indehar Statistics 12/10/2012 SITUATION: We are investigating the amount of cigarettes smoked by the population of smokers in the metropolitan area. The metropolitan area is defined as the greater metro area of Minneapolis and Saint Paul, and near suburbs. Furthermore, we are attempting to make inferences on meaningful and relevant correlations and trends within the data, among other insights in to the population. SAMPLING METHOD/DATA COLLECTION
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coefficient of determination, and the standard error of estimate. Conduct a test of hypothesis to determine whether the coefficient of correlation in the population is zero. Calculate the least squares regression line. Construct and interpret confidence and prediction intervals for the dependent variable. 2 Regression Analysis - Introduction Recall in Chapter 4 the idea of showing the relationship between two variables with a scatter diagram was introduced. In that case we showed
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H0by setting αequal to .10, .05, .01, and .001. How much evidence is there that the slope (regression relationship) is significant? f. Calculate the 95% confidence interval for β1using numbers on the output. Interpret the interval. g. Calculate the 99% confidence interval for β1 using numbers on the output. Interpret the interval. h. Find sb0and the t statistic for testing the significance of the y intercept on the output, and report their values. Show how t has been calculated by
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CASE 49: PROPERTY CRIMES I. Executive summary The focus of this study is the examination of the data provided by U.S government agencies. Our analysis revealed that of the eight possible contributing factors, only three variables (namely, urbanization rate, high school dropout rate, and population density) affected property crime rates. Our data analysis model accounted for approximately 66% of the factors contributing to property crimes. The model is generally considered to be statistically
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QMM 241 – HW#5 – Multiple Regression 13.21 – Data It is cross-sectional and the unit of observation is a single Noodles restaurant. 13.22 – Data conditioning Yes the X and Y data is well-conditioned. The Y variable will be the Sales/SqFt which we will be able to figure out with a formula containing all the X variables and the variable magnitudes are all similar in all categories. 13.23 – Predicted signs Variable | Sign | Reason | Seats-Inside | Positive | The larger the size of the restaurant
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average death rate and the predicted death rate. In other word, predicted death will be off of 98.35. 7) None of the P values is statistically significant because they are greater than level significance. 8)The interval for the variable beer does not fall within the confidence interval which is -1.24418 and 4.1060 9) 10) This model can be improved if the significance level is greater than the significance
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the lowest response is Dallas. The most reliable cities, according to their regressions, are Chicago and Denver; they follow most closely to the linear model. Below is a table used to show our data. City | Coefficient | P-Value | Confidence Intervals | R-Squared | Baltimore | -34,955.599 | 6.76 E-25 | -40,529.092,-29,382.106 | 0.499 | Dallas | -33,527.186 | 9.62 E-13 | -42,037.218,-25,017.154 | 0.282 | Chicago | -331,151.768 | 4.25 E-49 | -361,059.875,-301,243.661 | 0.756 | Denver
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Debt- have an effect on the dependent variable, i.e. the percentage of salary invested. Consequently the below tasks will be fulfilled. Step 1-Extracting a sample of 50 couples Step 2-Constructing histograms and point estimates with given confidence intervals Step 3-Inference from pivot tables to explain the preferences of different couples on investments based on independent variables Step 4-Performing multivariate regression and conducting significance tests on beta coefficients R^2 and F-tests
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According to the equation the Y value would be the amount charged (dependent variable) and the X value would be the annual income (independent variable) Looking at the results of regression we see that the Model is Significant with more than a 95% confidence level. According to the Residual
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一、第九章 操作：analyze→one sample T test→T value＝100 1) H0: μ = 100 H1: μ≠100 2) one-sample test表格抄下来 = 0.05，n = 25, df = 25-1=24 3) confidence level of 95%: lower≤X±tα2Sn≤upper 4) is unknown, so use a t statistic: tSTAT=X-μSn=2.140 5) P-value=sig= >0.05 Decision: Do not reject H0 6) Conclusion: insufficient evidence that true mean cost is different than $168. 二、第十章 操作：independent sample→t test→①Local 检验变量 ②Chain(1,2) 拆分变量 ①H0: σ12＝σ22 H1:σ12≠σ22 ②FSTAT=S12S22=0.01 sig=0
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