**EPEF009 Statistics for Decision Making TP Assignment Sample Singapore**

In EPEF009 Statistics for Decision Making course, you will learn how to use statistics to make better decisions. You will learn how to gather data, analyze it, and apply it to real-world scenarios. This course is designed for students who want to improve their decision-making skills. You will have the opportunity to use Excel and SPSS to analyze data, and you will also learn how to read and interpret research articles. By the end of this course, you will be able to make informed decisions based on statistical analysis.

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**Describe the role of statistics, its applications, and the basic statistical terms.**

Statistics play an important role in many different fields, from marketing to medicine. By analyzing data, statisticians can help to answer questions and solve problems. For example, a marketing team might use statistics to figure out which products are selling well and which ones need to be improved. In the medical field, statistics can be used to track the spread of disease and to develop new treatments.

Basic statistical terms include mean median, mode, range, and standard deviation. These terms can be used to describe the data in a given set. For instance, the mean is the average value of all the data points in a set, while the median is the middle value. The mode is the most common value, and the range is the difference between the highest and lowest values.

Standard deviation measures how far individual data points deviate from the mean. By understanding these basic terms, we can begin to see how statistics can be used to understand complex data sets.

**Apply graphical and computational methods to describe qualitative and quantitative data.**

Data can be represented in many different ways, depending on the type of data and the goals of the person presenting it. For example, categorical data can be represented using a bar chart, while numerical data can be represented using a line graph. However, some data sets are too large or complex to be accurately represented using traditional graphical methods. In these cases, computational methods may be used instead.

Computational methods involve using computers to perform complex mathematical operations on data sets. These methods can provide a more accurate representation of the data, but they can also be more time-consuming and difficult to interpret. Ultimately, the best way to represent data depends on the type of data and the goals of the person presenting it.

**Explain the basic concepts of probability.**

Probability is the study of how likely it is for an event to occur. The basic concepts of probability can be difficult to understand at first, but they are essential for making sound decisions in everyday life. For example, when deciding whether or not to take an umbrella with you before leaving for work, you are implicitly estimating the probability of rain.

The concept of probability can also be applied to more complex situations, such as stock market analysis. By understanding the basic principles of probability, you will be better equipped to make informed decisions in a wide variety of contexts.

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**Apply discrete probability distributions.**

In probability theory, a discrete probability distribution is a probability distribution characterized by a probability mass function. Probability mass functions are defined over the discrete elements of a sample space. A discrete probability distributions is often used to model the probabilities of outcomes of a random variable that can only take on a finite set of values, such as the roll of a die or the occurrence of an earthquake.

In contrast, a continuous probability distribution is used to model the probabilities of outcomes of a random variable that can take on any value within a range, such as the height of a person or the temperature outside. While discrete and continuous distributions are both important tools in probability theory, they each have their own advantages and disadvantages.

Discrete distributions are often easier to work with mathematically, but they may be less accurate when modeling real-world phenomena. Continuous distributions, on the other hand, can be more difficult to work with but may provide a more accurate representation of reality. When choosing which type of distribution to use, it is important to consider the nature of the data being modeled and the desired level of accuracy.

**Apply continuous probability distributions.**

In probability theory, a discrete probability distribution is a probability distribution characterized by a probability mass function. Probability mass functions are defined over the discrete elements of a sample space. A discrete probability distributions is often used to model the probabilities of outcomes of a random variable that can only take on a finite set of values, such as the roll of a die or the occurrence of an earthquake.

In contrast, a continuous probability distribution is used to model the probabilities of outcomes of a random variable that can take on any value within a range, such as the height of a person or the temperature outside. While discrete and continuous distributions are both important tools in probability theory, they each have their own advantages and disadvantages. Discrete distributions are often easier to work with mathematically, but they may be less accurate when modeling real-world phenomena.

Continuous distributions, on the other hand, can be more difficult to work with but may provide a more accurate representation of reality. When choosing which type of distribution to use, it is important to consider the nature of the data being modeled and the desired level of accuracy.

**Apply point and interval estimates.**

In statistics, point and interval estimates are used to estimate the value of a population parameter. Point estimates are single values that are used to estimate a population parameter, such as the mean or median. Interval estimates are ranges of values that are used to estimate a population parameter. For example, a 95% confidence interval is an interval estimate that contains the true value of the population parameter 95% of the time. To apply point and interval estimates, one must first choose a sample statistic (such as the sample mean) that will be used to estimate the population parameter.

The chosen statistic should be unbiased and have low variance. Next, one must choose a level of confidence (such as 95%) and calculate the margin of error. The margin of error is used to create the confidence interval, which is then used to make predictions about the population parameter. Finally, one must interpret the results in terms of the level of confidence chosen. For example, if a 95% confidence interval contains the value 10, one can say with 95% confidence that the true value of the population parameter falls between 10 and 10 + margin of error.

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**Apply hypothesis testing techniques.**

Hypothesis testing is an important statistical tool that allows us to make inferences about a population based on a sample. There are two types of hypothesis tests: one-tailed and two-tailed. In a one-tailed test, we are interested in testing whether the population mean is greater than, less than, or equal to a specific value. In a two-tailed test, we are interested in testing whether the population mean is different from a specific value. To conduct a hypothesis test, we first need to state our null and alternative hypotheses. The null hypothesis always states that there is no difference between the population parameter and the specific value that we are testing against. The alternative hypothesis states that there is a difference between the population parameter and the specific value. We then calculate a test statistic using our sample data. This test statistic tells us how likely it is that our results occurred by chance if the null hypothesis is true.

Finally, we compare our test statistic to a critical value to determine whether or not to reject the null hypothesis. If we reject the null hypothesis, this means that our results are statistically significant and that we can conclude that there is a difference between the population parameter and the specific value. If we do not reject the null hypothesis, this means that our results are not statistically significant and that we cannot conclude that there is a difference between the population parameter and the specific value.

**Apply simple linear regression analysis.**

Simple linear regression is a statistical method that allows us to predict the value of a dependent variable based on the value of an independent variable. In other words, it allows us to determine how changes in the independent variable are related to changes in the dependent variable. To perform a simple linear regression analysis, we need to collect data on both the independent and dependent variables.

Once we have this data, we can use it to generate a mathematical model that describes the relationship between the two variables. This model can then be used to make predictions about the dependent variable based on new values of the independent variable. In conclusion, simple linear regression is a powerful statistical tool that can be used to gain insights into the relationships between different variables.

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