Confidence interval 2 sample t test,what is the secret of photo 51,the secret circle episode 5 online cheats,change management itil questions 50 - Tips For You

Sample Size Calculator, Confidence Interval, Level, Population - Sample Size Calculator, Sample Size, Confidence Interval, Confidence Level, Population Size, Normal Distribution, Statistics,. Sample Size Calculator - SurveyMonkey - The probability that your sample accurately reflects the attitudes of your population. National Statistical Service ยง Sample Size Calculator - NSS - Please Note: This calculator should be used for simple random samples only.
Calculating A Sample Size Based On A Confidence Level - Mathematics Stack Exchange is a question and answer site for people studying math at any level and professionals in related fields. Mark has done a great job in writing complex statistical concepts in an easy to understand format that makes grasping them both easy to understand and to use. With the help of Mark's book, and some diligent studying, I received an A in my stats course.
Excel Statistical Master that taught me all of the basic concepts for the different tests we used.
Unlike the indecipherable jargon in the countless books I have wasted money on, the language in this book is plain and easy to understand. This same problem above is solved in the Excel Statistical Master with only 3 Excel formulas (and NO looking anything up on a Z Chart).
This same problem above is solved in the Excel Statistical Master with only 3 Excel formulas (and not having to look anything up on a Z Chart). This same problem above is solved in the Excel Statistical Master with only 1 Quick Excel formula (and NO looking anything up on a Z Chart).
This same problem above is solved in the Excel Statistical Master with only 2 Excel formulas (and not having to look anything up on a Z Chart).
This would state that the actual population Proportion has a 95%probability of lying within the calculated interval.
Objective: This example compares the A-level count for a sample of Oxford University students getting BA and BSc degrees.
The assumptions are assessed initially using the A-level histogram conditioning on the Degree Type. The relatively high positive skewness value and the potential outlier suggests that normality is not a viable assumption for the A-level count of BA degree students.
The assumption of normality for students in BSc programs can be assessed by clicking on the right of the conditioning slider in each of the above two plots.
The sample distribution for BSc student A-counts is not well approximated by the normal curve, but it has somewhat less skweness than that of BA students.
The BSc A-count data appears to follow a follow a straight line until the right-hand tail is reached.
Normality does not seem to be a viable assumption for the A-counts of either the two student groups.
The assumptions of the two-sample CI are not satisfied and a major problem is the presence of outliers. We want to determine if the distribution of the BA A-level counts shifts relative to that of the BSc A-level counts.
The two-sample confidence interval is constructed from the A-level by Degree Type dot plot. The means and standard errors are given in the Means (StdErrs) report and they are visualized in the dot plot as means and error bars (shown in green). A formal way of assessing population mean differences is by constructing a two-sample CI on µBA - µBSc.
The estimated population mean difference is greated than 0 using any reasonable confidence level.
Excel is the widely used statistical package, which serves as a tool to understand statistical concepts and computation to check your hand-worked calculation in solving your homework problems. This site provides illustrative experience in the use of Excel for data summary, presentation, and for other basic statistical analysis. Microsoft Excel 2000 (version 9) provides a set of data analysis tools called the Analysis ToolPak which you can use to save steps when you develop complex statistical analyses. If the Data Analysis command is selectable on the Tools menu, then the Analysis ToolPak is installed on your system.
Microsoft Excel is a powerful spreadsheet package available for Microsoft Windows and the Apple Macintosh. To save your document with its current filename, location and file format either click on File - Save.
When you start Excel, a blank worksheet is displayed which consists of a multiple grid of cells with numbered rows down the page and alphabetically-titled columns across the page. It is important to be able to move around the worksheet effectively because you can only enter or change data at the position of the cursor. Note that the size of a scroll box indicates the proportional amount of the used area of the sheet that is visible in the window.
To select several cells which are not adjacent, press "control" and click on the cells you want to select. One worksheet can have up to 256 columns and 65,536 rows, so it'll be a while before you run out of space. Note that as you type information into the cell, the information you enter also displays in the formula bar. If you are creating a long worksheet and you will be repeating the same label information in many different cells, you can use the AutoComplete function. Numbers are assumed to be positive; to enter a negative number, use a minus sign "-" or enclose the number in parentheses "()".
An entry interpreted as a value (number, date, or time) is aligned to the right side of the cell, to reformat a value. Select a cell in the region, and press Ctrl+Shift+* (in Excel 2003, press this or Ctrl+A) to select the Current Region.
Repeat step 4, select a different color than you selected for Condition 1, and then click OK. Note: Be sure to distinguish between absolute reference and relative reference when entering the formulas. Problem: Rounding all the numbers in column A to zero decimal places, except for those that have "5" in the first decimal place. Copying the entire sheet means copying the cells, the page setup parameters, and the defined range Names. In the Sort by option of the Sort dialog box, select the row number by which the columns will be sorted and click OK.
The Data Analysis ToolPak has a Descriptive Statistics tool that provides you with an easy way to calculate summary statistics for a set of sample data. Enter A1:A4 in the input range box, A1 is a value in column A and row 1, in this case this value is 2.
Consider the problem of finding the probability of getting less than a certain value under any normal probability distribution.
A: What is the probability that a randomly selected student score will be less than 600 points?
C: What is the probability that a randomly selected student score will be between 400 and 600? Hint: Using Excel you can find the probability of getting a value approximately less than or equal to a given value. As you see the value 0.84134474 appears in A1, indicating the probability that a randomly selected student's score is below 600 points. You can use NORMINV from the function box to calculate a value for the random variable - if the probability to the left side of this variable is given.
At the end of this screen you will see the formula result which is approximately 628 points. Now we would like to show how Excel is used to develop a certain confidence interval of a population mean based on a sample information.
After entering the data, we followed the descriptive statistic procedure to calculate the unknown quantities. On the other hand, we can say that of all the intervals formed this way 95% contains the mean of the population.
If the sample n is less than 30 or we must use the small sample procedure to develop a confidence interval for the mean of a population. Now you would like to see how Excel is used to develop a certain confidence interval of a population mean based on this small sample information.
Again, the only things you have to do are: add the margin of error to the mean of the sample,, find the upper limit of the interval and to subtract the margin of error from the mean to find the lower limit of the interval. To demonstrate how Excel finds these quantities we will use the data set, which contains the hourly incomes of 10 work-study students here, at the University of Baltimore. After entering the data we follow the descriptive statistic procedure to calculate the unknown quantities (exactly the way we found quantities for large sample). Now, like the calculation of the confidence interval for the large sample, calculate the confidence interval of the population based on this small sample information.
In this section you wish to know how Excel can be used to conduct a hypothesis test about a population mean. The null hypothesis indicates that the average hourly income of a work-study student is equal to $7 per hour; however, the alternative hypothesis indicates that the average hourly income is not equal to $7 per hour.

I will repeat the steps taken in descriptive statistics and at the very end will show how to find the value of the test statistics in this case, z, using a cell formula. Using steps taken the large sample size case, Excel can be used to conduct a hypothesis for small-sample case. The null hypothesis indicates that average hourly income of a work-study student is equal to $7 per hour .The alternative hypothesis indicates that average hourly income is not equal to $7 per hour. In this section we will show how Excel is used to conduct a hypothesis test about the difference between two population means assuming that populations have equal variances. In this section we will show how Excel is used to conduct a hypothesis test about the difference between two population means. We can also draw this conclusion by comparing the p-value for a two-tail test and the alpha value. In this section the objective is to see whether or not means of three or more populations based on random samples taken from populations are equal or not. In this case we were interested to see whether there a significant difference among hourly wages of student assistants in three different service departments here at the University of Baltimore. In this section, the study involves six students who were offered different hourly wages in three different department services here at the University of Baltimore.
The CHI-SQUARE distribution can be used in a hypothesis test involving a population variance. In this example the objective is to see whether or not based on a randomly selected sample information the standards set for a population is met. As you see the observed sample numbers for groups two and three are lower than their expected values unlike group one which has a higher expected value. Step 1.Select a cell in the work sheet, the location which you like the p value of the CHI-SQUARE to appear. Step 3.Refer to function category box and choose statistical, from function name box select CHITEST and click on OK. The CHI-SQUARE distribution is also used to test and see whether two variables are independent or not.
In this section we would like to examine whether or not the variances of two populations are equal. Now let see whether or not the variances of hourly income of student-assistant and work-study students based on samples taken from populations previously are equal.
In this section the objective is to see whether there is a correlation between two variables and to find a model that predicts one variable in terms of the other variable. Now lets use Excel to find the linear correlation coefficient and the regression line equation. As you see the correlation between the number of cases of soda demanded and the temperature is a very strong positive correlation. Moving Average Models: Use the Add Trendline option to analyze a moving average forecasting model in Excel. Click on the chart to select it, and click on any point on the line to select the data series. Exponential Smoothing Models: The simplest way to analyze a timer series using an Exponential Smoothing model in Excel is to use the data analysis tool.
Select "Descriptive Statistics" by pointing at it and clicking twice, or by highlighting it and clicking on the "Okay" button. Variance: The variance of a set of data is the average of the cumulative measure of the squares of the difference of all the data values from the mean. To construct the probability function for bank robberies, first define the random variable x, bank robbery take.
Normal Example: If the time required to complete an examination by those with a certain learning disability is believed to be distributed normally, with mean of 65 minutes and a standard deviation of 15 minutes, then when can the exam be terminated so that 99 percent of those with the disability can finish?
Solution: Because the average and standard deviation are known, what needs to be established is the amount of time, above the mean time, such that 99 percent of the distribution is lower. Alternatively, instead of standardizing with the Z distribution using Excel we can simply work directly with the normal distribution with a mean of 65 and standard deviation of 15 and enter "=NORMINV(0.99,65,15)". Another Example: In the early 1980s, the Toro Company of Minneapolis, Minnesota, advertised that it would refund the purchase price of a snow blower if the following winter's snowfall was less than 21 percent of the local average. Solution: Within limits, snowfall is a continuous random variable that can be expected to vary symmetrically around its mean, with values closer to the mean occurring most often. If a random sample of size n = 1 (day) is selected, what is the probability this x value will exceed 28?
If a random sample of size n = 4 (days) is selected, what is theprobability that xbar ³ 28? From this we can see that five countries with the same speed limit have very different positions on the safety list.
Solution: Enter the ten paired y and x data into cells A2 to A11 and B2 to B11, with the "death" rate label in A1 and "speed" limits label in B1, the following steps produce the regression output. Because the Y and X ranges include the "Death" and "Speed" labels in A1 and B1, select the "Labels" box with a click. Click the "Output Range" button and type reference cell, which in this demonstration is A13. To get the predicted values of Y (Death rates) and residuals select the "Residuals" box with a click.
Under the "ANOVA" section are the estimated regression coefficients and related statistics that will be discussed in detail in the next chapter. This Sample Size Calculator is presented as a public service of Creative Research Systems survey software. I purchased the materials about halfway through the course and wish I had known about this manual from the start of the class! You'll be able to grasp your statistics course a LOT easier with the Excel Statistical Master. If you found your statistics book confusing, You'll really like the Excel Statistical Master. The researcher believes the mean A-level count for BA students will be higher than that of BSc students. The raw data is given in Appendix A of Rees and is reproduced as a set of variables in StatObjects. Click on the Moments triangular reveal button and select Normal Density from the Histogram popup menu. The four student's with the highest A-counts are indeed outliers and student #38 is a borderline extreme outlier. The following plot is constructed by clicking on the Means and Pairwise CIs triangular reveal buttons and by selecting Fit Means from the DotPlot popup menu.
The error bar (sample mean ± the standard error) for the A-level count of BA students is completely above the overall mean reference line, whereas the error bar for BSc students is completely below the reference line. The default 95% CI does not contain 0 which means that 0 is not a plausible value of the population mean difference.
However, this interpretation is weakened by our knowledge that the assumptions are violated. It can be opened either by selecting Start - Programs - Microsoft Excel or by clicking on the Excel Short Cut which is either on your desktop, or on any PC, or on the Office Tool bar. You can move the cursor by using the arrow keys or by moving the mouse to the required cell and clicking.
The position of a scroll box indicates the relative location of the visible area within the worksheet.
You can also enter information into the formula bar, and the information will appear in the selected cell. This function will look at other entries in the same column and attempt to match a previous entry with your current entry. Right click on a cell, then select "Pick From List." This will give you a menu of all other entries in cells in that column. The Move or Copy dialog box enables one to copy the sheet either to a different location in the current workbook or to a different workbook. Select the Tools *pull-down menu, if you see data analysis, click on this option, otherwise, click on add-in..
In a problem, when the mean and the standard deviation of the population are given, you have to use common sense to find different probabilities based on the question since you know the area under a normal curve is 1. After clicking on the Function option, the Paste Function dialog appears from Function Category. We will use the hourly incomes of different work-study students than those introduced earlier in the confidence interval section. Let's use the hourly income of 10 work-study students at UB to conduct the following hypothesis. The Excel procedure for testing the difference between the two population means will require information on the variances of the two populations. Assuming independents samples are taken from normally distributed populations with equal variances, Excel would do this analysis if you choose one way anova from the menus. This means there is a significant difference between means of hourly incomes of student assistants in these departments.
However, in this section we would like to test and see how close a sample results are to the expected results.

From the menus, select insert then click on the Function option, Paste Function dialog box appears.
For example based on sample data you might want to see whether smoking and gender are independent events for a certain population. When Paste Function dialog box appears, click on Statistical in function category and CHITEST in the function name then click OK.
To find the chi-square value, use CHINV function, when Chinv box appears enter 0.477395 for probability part, then 1 for the degrees of freedom. There are so many examples that we could mention but we will mention the popular ones in the world of business. Referring to this expression we can approximately predict the number of cases of soda needed on June 14.
The sample variance is simply the arithmetic mean of the squares of the difference between each data value in the sample and the mean of the sample.
Assuming 60 percent of those caught lose their entire take and 40 percent lose half, graph the probability mass function using EXCEL.
This probability tree diagram shows the "branches" that must be followed to show the calculations captured in the binomial mass function for n = 4 and = 0.2. In general to obtain the x value for which alpha percent of a normal random variable's values are lower, the following "NORMINV" command may be used, where the first entry is a, the second is m , and the third is s.
If the average snowfall is 45.25 inches, with a standard deviation of 12.2 inches, what is the likelihood that Toro will have to make refunds?
Using Excel, this normal probability is obtained with the "NORMDIST" command, where the first entry is x, the second is mean m , the third is standard deviation s, and the fourth is CUMULATIVE (1). The sampling distribution of the sample mean xbar is normal with a mean of 24 and a standard error of the mean of 4. The sampling distribution of the sample mean xbar is normal with a mean of 24 and a standard error of the mean of 2 using Excel, 0.02275 =1-NORMDIST(28,24,2,1). The two-sample assumptions of normality and equality of variances are checked before the two-sample confidence interval is computed. This is confirmed by looking at the BSc A-level count normal quantile plot which is displayed by clicking on the right-hand side of the conditioning slider. This is because some of the value are repeated which in this case means that two of the outliers have the same value (students #6 and #28 from the normal quantile plot). However, this does by itself not provide adequate support for the notion that the population means differ.
If you have made any changes since the file was last saved, you will be asked if you wish to save them. Once selected the cell becomes the active cell and is identified by a thick border; only one cell can be active at a time. Click OK on the descriptive statistics dialog, click on Summary Statistic, click on the confidence interval level and type in 90% or in other problems whichever confidence interval you desire. I collected the hourly income data of 36 randomly selected work-study students and 36 student assistants. Since the variances of the two populations are unknowns they should be replaced with sample variances.
We can also draw this conclusion by comparing the p-value for a two tail -test and the alpha value. Similar to the above case, the data in this case are taken from various offices here at the University of Baltimore. We can also choose Anova: two way factor with or without replication option and see whether there is significant difference between means when different factors are involved. For example it is assumed the guidelines for hiring people with different ethnic background for the US government is set at 70%(WHITE), 20%(African American) and 10%(others), respectively. Since this value, F=1.984615385, falls in acceptance area we fail to reject the null hypothesis. Usually independent variable is presented by the letter x and the dependent variable is presented by the letter y. On the other hand, the formula for an estimate for the variance in the population is similar to the formula for the sample variance, except that the denominator in the fraction is (n-1) instead of n. If criminals make their decisions strictly on this expected value, then it pays to rob banks. Next to these coefficient estimates is information on the variability in the distribution of the least-squares estimators from which these specific estimates were drawn: the column titled "Std.
Example #1 in the two-sample hypothesis testing module will examine this problem from a testing perspective.
The normal quantile plot has a more revealing way of checking the efficacy of the equality of variances assumption.
In the future, the plot will support jittering which will allow the x-values to be separated slightly. Select 99% from the Level popup menu in the Pairwise CIs report and observe that the lower limit is still above but very close to 0. The hourly income range for work-study students was $6 - $8 while the hourly income range for student assistants was $6-$9.
I collected hourly income data of 11 randomly selected work-study students and 11 randomly selected student assistants. Another example in this situation could involve the age range of an individual and his or her smoking habit. The following figure shows the graph of an F distribution with 10 degrees of freedom for both the numerator and the denominator.
Hence, the sample results do support the conclusion that student assistants hourly income variance is equal to the work study students hourly income variance. A business man would like to see whether there is a relationship between the number of cases of sold and the temperature in a hot summer day based on information taken from the past.
However, you should not worry about this difference if the sample size is large, say over 30. A decision rule based only on an expected value, however, ignores the risks or variability in the returns. Draw a tree diagram showing the different ways in which a test taker could get 0, 1, 2, 3 and 4 correct answers. A range may be typed in or selected by highlighting the cells with the mouse after clicking on the box. Error" contains the standard deviations (standard errors) of the intercept and slope distributions; the "t-ratio" and "p" columns give the calculated values of the t statistics and associated p-values.
The following plot is obtained by clicking on the Quantiles triangular reveal button and selecting Robust Fit and Quantile Lines from the QuantPlot popup menu.
Since the slope of the robust fit line in the normal quantile plot estimates the standard deviation, the user can click back and forth on the ends of the conditioning slider to see if the slope changes. On the other hand if we were asked to do this problem by hand, we would have had to calculate the x value using the normal distribution formula x = m + zd. The main objective in this hypothesis testing is to see whether there is a significant difference between the means of the two populations. Similar to case one data may appear in a table but unlike the case one this table may contains several columns in addition to rows.
He also would like to estimate the number cases of soda which will be sold in a particular hot summer day in a ball game.
In addition, our expected value calculations do not include the cost of jail time, which could be viewed by criminals as substantial.
If the dialog box blocks the data, it can be moved on the screen by clicking on the title bar and dragging.
The NULL and the ALTERNATIVE hypothesis is that the means are equal and the means are not equal, respectively. The shape of an F distribution is positively skewed and depends on the degrees of freedom for the numerator and the denominator.
He clearly recorded temperatures and number of cases of soda sold on those particular days. The slope changes very little (although the BA slope is somewhat steeper) which would indicate that the estimates of the standard deviations are not that different. To find the value of each cell in the new table we should multiply the sum of the cell column by the sum of the cell row and divide the results by the grand total. In this situation the variable y (the dependent variable) is the number of cases of soda and the x (independent variable) is the temperature. As a result, the problem causing inequality of variances and lack of normality is at least partially due to outliers.
To calculate the expected frequency for a category, you can multiply the proportion of that category by the sample size (in here 1000). Now based on the following table test whether or not the smoking habit and gender of the population that the following sample taken from are independent.

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