BUS105 Statistics is a required course for business majors at many universities. The focus of the course is on the use of statistics to make sound business decisions. Topics covered in the course include data collection, descriptive statistics, probability, and inferential statistics.

This course is important for business students because it teaches them how to analyze data and make informed decisions based on that data. In today’s business world, it is essential to be able to understand and use statistical information correctly. BUS105 Statistics provides students with the skills they need to do just that.

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In this section, we discuss some assignment activities. These are:

BUS105 Assignment Activity 1: Describe statistical data.

Statistical data is a collection of numerical values that represent a sample of information from a larger population. The purpose of statistical data is to help inform decisions by providing insights into what the data may suggest about the larger population.

Statistical data can be displayed in graphical or tabular form, and different types of graphs and charts can be used to communicate different aspects of the data. Some common types of graphs used to display statistical data are histograms, frequency polygons, and box plots.

Assignment Activity 2: Define probability, mean and standard deviations for a probability distribution.

Probability is a measure of the likelihood that an event will occur. Probability is often expressed as a decimal or percentage value between 0 and 1, with 0 indicating that an event is impossible and 1 indicating that an event is certain to occur.

The mean is the average value of a set of data. To calculate the mean, add up all the values in the set and then divide by the number of values in the set.

The standard deviation is a measure of how spread out the values in a set of data is. The standard deviation is calculated by taking the square root of the variance. The variance is calculated by taking the average of the squared differences from the mean.

Assignment Activity 3: Explain the probabilities for the sample mean and proportion.

In statistics, the sample mean and proportion is two important measures that are used to describe a population. The sample mean is the arithmetic mean of a sample, while the proportion is the percentage of individuals in a sample that fall into a certain category. Both measures can be used to estimate population statistics, but it is important to understand how they are calculated and what their limitations are.

The Sample Mean

The sample mean is a statistic that represents the average value of a variable in a sample. It is calculated by adding all of the values of the variable in the sample and then dividing by the total number of values in the sample. For instance, if we have a sample of five people and their heights (in inches), we would add up all of the heights and then divide by five to get the sample mean.

The Sample Proportion

The sample proportion is a statistic that represents the percentage of individuals in a sample that fall into a certain category. It is calculated by taking the number of individuals in the sample that fall into the category and dividing it by the total number of individuals in the sample. For instance, if we have a sample of 100 people and 60 of them are women, then the sample proportion of women would be 60%.

BUS105 Assignment Activity 4: Identify the Confidence Interval for the mean and proportion of a population.

There are two types of confidence intervals: the mean and the proportion.

For the mean, a 95% confidence interval means that if we were to repeat our study over and over again, 95% of the time the true population means would fall within our calculated interval. 

For the proportion, a 95% confidence interval means that if we were to repeat our study over and over again, 95% of the time the true population proportion would fall within our calculated interval.

Assignment Activity 5: Execute the Hypothesis Testing (both one-sample and two-samples) for the population mean and proportion population.

There are two types of hypothesis tests: the one-sample and the two-sample.

The one-sample test is used when you want to compare a sample mean to a population mean. The two-sample test is used when you want to compare two means, such as the means of two different groups.

For both tests, the null hypothesis is that there is no difference between the sample mean and the population mean (or between the two group means). The alternative hypothesis is that there is a difference.

The one-sample test is usually used when you have a small sample size (n < 30). The two-sample test is usually used when you have a large sample size (n > 30).

To conduct a one-sample test, you will need to calculate the standard error and the z-score. The standard error is the standard deviation of the sampling distribution. It can be calculated using the following formula:

SE = s / √n

where s is the standard deviation of the sample and n is the sample size.

Assignment Activity 6: Apply an Analysis of Variance (ANOVA) procedure to compare the means of independent random samples.

When comparing the means of two or more independent groups, an ANOVA procedure is used. This procedure allows for the determination of whether there is a statistically significant difference between the means of the groups. In order to do this, the ANOVA procedure compares the variability within each group to the variability between groups.

If there is greater variability within groups than between groups, then it can be concluded that there is no statistically significant difference between the means of the groups. Conversely, if there is greater variability between groups than within groups, then it can be concluded that there is a statistically significant difference between the means of the groups.

The ANOVA procedure is conducted using the following steps:

1. Calculate the means for each group.
2. Calculate the variance for each group.
3. Calculate the pooled variance.
4. Calculate the F-ratio.
5. Compare the F-ratio to the critical value to determine whether there is a statistically significant difference between the means of the groups.

BUS105 Assignment Activity 7: Implement and fit a Linear Regression Line to a set of sample data and interpret the results.

There are a few steps involved in fitting a linear regression line to data. First, you need to have a set of data points that you can fit the line. This data can come from anything – recent sales figures, survey responses, etc. Once you have your data set, you need to choose which variable will be your dependent variable (the one you are trying to predict) and which will be your independent variable (the one that you are using to predict the dependent variable).

After that, it’s simply a matter of plugging the variables into a linear regression equation and solving for the slope and intercept. Once you have those values, you can plot the line on a graph and evaluate how well it fits the data.

The linear regression equation is:

Y = mX + b

where Y is the dependent variable, X is the independent variable, m is the slope of the line, and b is the y-intercept.

The slope of the line can be calculated using the following formula:

m = (∑XY – (∑X)(∑Y)) / (∑X2 – (∑X)2)

where ∑XY is the sum of the products of each X and Y value, ∑X is the sum of all X values, ∑Y is the sum of all Y values, and ∑X2 is the sum of the squares of all X values.

The y-intercept can be calculated using the following formula:

b = (∑Y – m(∑X)) / n

where n is the number of data points.

Once you have the slope and y-intercept, you can plug them into the linear regression equation and use them to make predictions about the dependent variable.

Assignment Activity 8: Interpret the results from Multiple Regression Analyses.

The results from multiple regression analyses can be interpreted in a variety of ways. The most important thing to remember is that these results are merely quantitative predictions; they cannot be used to directly infer causality. 

One way to interpret the results is by looking at the coefficient values for each predictor variable. A positive coefficient indicates that as the predictor variable increases, the response variable is also predicted to increase. A negative coefficient indicates that as the predictor variable increases, the response variable is predicted to decrease. The magnitude of the coefficients can be interpreted as an indication of how much impact that particular predictor has on the response variable. 

Another way to interpret multiple regression analysis results is through analysis of variance (ANOVA). This assesses whether the model as a whole is significant and whether each individual predictor variable is significant. A significant model means that there is a relationship between the predictor variables and the response variable; a non-significant model means that there is no such relationship. Individual predictor variables can be evaluated for significance by looking at the p-values associated with each one. A p-value below 0.05 indicates that the variable is significant. 

Interpreting multiple regression results can be difficult, so it is important to consult with a statistician or other qualified individual if you are unsure about how to proceed.

Assignment Activity 9: Use suitable computer software to perform data analyses according to the statistical concepts and techniques learned from this course including data summary and presentation, probability computation, confidence intervals, hypothesis tests, and linear and multiple regression analyses.

The computer software that I used to perform the data analyses in this course was Microsoft Excel. I found Excel to be very user-friendly and easy to use for all of the statistical concepts and techniques that I learned in this course.

Data summary and presentation:

Excel has a variety of features that can be used for data summary and presentation. For example, the “PivotTable” function can be used to create summary tables of data. The “Chart” function can be used to create graphs and charts.

Probability computation:

Excel has a number of built-in functions for probability computation, including the “NORMDIST” function for computing normal probabilities and the “BINOMDIST” function for computing binomial probabilities.

Confidence intervals:

Excel has a number of built-in functions for confidence interval computation, including the “NORMINV” function for computing normal confidence intervals and the “TINV” function for computing t-distribution confidence intervals.

Hypothesis tests:

Excel has a number of built-in functions for hypothesis testing, including the “CHISQ.TEST” function for performing chi-square tests and the “T TEST” function for performing t-tests.

Linear and multiple regression:

Excel has a number of built-in functions for linear and multiple regression, including the “LINEST” function for performing linear regression and the “TREND” function for performing multiple regression.

BUS105 Assignment Activity 10: Report and explain the outcome of a particular statistical analysis performed for decision-making.

The outcome of a particular statistical analysis can be extremely helpful for decision-making. By analyzing past data, analysts are able to provide understanding and predictions for what could happen in the future. This is especially important in business, where knowing what might happen can help executives make crucial decisions about investments, production levels, and marketing strategies. 

For example, let’s say a company is considering launching a new product. They may use statistical analysis to predict how successful the product will be based on past launches of similar products. This information can then be used to make decisions about production levels, pricing, and advertising. 

Statistical analysis can also be used to understand trends in customer behavior. By looking at patterns in data, analysts can predict how likely customers are to make certain decisions, such as switching to a competitor’s product. This information can be used to inform marketing and sales strategies.

Assignment Activity 11: Summarise statistical analysis and findings through oral presentations in class or on recorded video.

Statistical analysis and findings can be summarised in oral presentations in class or on recorded video. In either case, it is important to be clear and concise while also being accurate and thorough.

When summarising statistical analysis and findings, it is helpful to start with a brief overview of the study or data set. This should include the purpose of the study or data set, the population that was studied, the methods used, and the main results.

Next, you should discuss specific findings from the study or data set. Be sure to describe how each finding was calculated and what it means in terms of the overall research question. Finally, you should provide a conclusion that highlights the most important findings from the study or data set.

Assignment Activity 12: Demonstrate the essential knowledge and interpersonal skills to work effectively in a team.

There are many essential knowledge and interpersonal skills required to work effectively in a team. In order to be an effective team member, it is important to have good communication skills, be able to collaboratively work with others towards a common goal, and possess strong conflict resolution skills. It is also essential to have knowledge of the specific goals and objectives of the team in order to contribute effectively. Interpersonal skills such as these are essential for any team member in order to help the team function smoothly and achieve its objectives.

BUS105 Assignment Activity 13: Show well-developed written proficiency in statistical reports.

Statistical reports are an important part of any business. By compiling data and analyzing it, businesses can make better decisions about where to allocate their resources and how to improve their products and services.

As a business writer, you should be well-versed in the art of writing statistical reports. This means being able to compile data accurately and presenting it in a clear and concise manner. It also means being able to understand complex statistical concepts and translate them into language that non-experts can understand.

Above all, it’s important to be accurate and truthful in your reporting. Businesses rely on statistical reports to make informed decisions, so it’s critical that the data presented is accurate and unbiased.

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