a) A company is interested in providing a new benefit to its employees to encourage them to utilize public transportation. To study the potential of providing this benefit‚ the company would like to do a survey of the workers at a company to gather information on why do or do not use public transportation for commuting to work. The company is about 25‚000 people in areas where public transportation is available. You also want to find out the reasons why their choice is what it is. In this case
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45.16 + 2.3263 * 10 = 99% of households spent less than $68.42 NORMINV (.99‚ 45.16) References Levine‚ D.‚ Stephan‚ D.‚ Krehbiel‚ T.‚ & Berenson‚ M. (2008) Statistics for managers using Microsoft Excel w/cd. (5th ed.). Upper Saddle River‚
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Phase 3 IP CTU Online MGMT600-1301A-05: Applied Managerial Decision-Making To use the Chi-square distribution‚ the quantitative variable sales volume has to be converted into categories or groups such as “Achieved Target” vs. “Did not achieve target”. 1) A chi-square test that can be carried out in this context is to see whether the automated contact system affects whether or not the sales force achieve their targets. Null Hypothesis (HO): There is no relationship between
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Applied Research and Statistics QNT561 Research and Sampling Designs Shindeera Robinson June 21‚ 2010 Chapter 8 21. What is sampling error? Could the value of the sampling error be zero? If it were zero‚ what would this mean? Sampling error is the difference between the statistic estimated from a sample and the true population statistic. While we would expect the sampling error to not be zero‚ it is not impossible. For example if you were evaluating the ethnicities of a population and everyone
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1 1. Statistics is the science of collecting‚ organizing‚ analyzing‚ interpreting‚ and presenting data. TRUE 2. Inferential statistics refers to generalizing from a sample to a population‚ estimating unknown parameters‚ drawing conclusions‚ and making decisions. TRUE 3. Descriptive statistics refers to summarizing data rather than generalizing about the population. FALSE 4. Estimating parameters and testing hypotheses are important aspects of descriptive statistics. FALSE
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References: Needles‚ B. E. (2010). Managerial Accounting. Florida: Cengage Learning. Warren‚ C. S. (2013). Managerial Accounting. Chicago: Cengage Learning.
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chain store AJ DAVIS. The 1st individual variable considered is Location. It is a qualitative variable. The three subcategories are Urban‚ Suburban and Rural. Since this is a qualitative variable‚ the measures of central tendency and descriptive statistics has not been computed for this variable. The frequency distribution and pie chart are given as follows: Frequency Distribution: | Location | Frequency | Urban | 21 | Suburban | 15 | Rural | 14 | From the frequency distribution and pie
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for all the 50 states of the US (i.e. sample size of 50) and were gathered from various sources‚ including a variety of US government sources‚ among which are: the 1988 Uniform Crime Reports‚ Federal Bureau of Investigation‚ Office Research and Statistics‚ Social Security Administration‚ The Commerce Department and other government sources. The variables analyzed are as follows: CRIMES - Property crime rate per hundred thousand inhabitants (includes burglary‚ larceny‚ theft‚ and motor vehicle theft)
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9.54 (a) At the .01 level of significance‚is the true mean greater than 10? Null Hypothesis: Ho: M = 10 :Mean is 10 or less pages Alternative Hypothesis: H1: M >10 :Mean is more than 10 pages Significance level=alpha (a) = 0.01 or 1% No of tails= 1 This is a 1-tailed test because we are testing that the mean is greater than 10 Since sample size= 35 >= 30 we will use normal distribution This is a one tailed test because we are testing the area in the right tail Since
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A homeowner recorded the amount of electricity in kilowatt-hours (KWH) consumed in his house on each of 21 days. He also recorded the numbers of hours his air conditioner (AC) was turned on and the numbers of times his electric clothes dryer (DRYER) was operated. His objective was to relate the KWH consumption to the AC and DRYER usage. In addition‚ he wanted to know how many KWH’s the AC used per hour and the number of KWH’s used in each run of the DRYER. Statistical regression analysis can serve
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