GSCI1003 Truth And Lies: Understanding Statistics Assignment Sample Canada

In this course, you will learn to think critically by understanding statistics. You’ll develop your ability for logical thinking and improve your facility with numbers as well. The course will also teach you how to better understand and use statistical information in the media. Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. It deals with all aspects of this, including the planning of data collection in terms of the design of surveys and experiments.

Statistics is used in a wide variety of occupations and helps people identify, study, and solve many real-world problems. Some examples of fields that use statistics include:

  • Business: Statistics is used in marketing and market research to determine what products people want and how much they are willing to pay for them. It is also used in financial analysis to predict stock prices and trends.
  • Medicine: Statistics is used in epidemiology to study the spread of diseases, in clinical trials to test new treatments, and in medical research to develop new ways of diagnosing and treating diseases.
  • Biology: Statistics is used in ecology to study the distribution of species, in evolution to study the changes in genetic frequencies over time, and in agriculture to study the yield of crops.

The statistical theory defines a statistic as a function of a sample where the function produces an estimate of a population parameter. A statistic is used to make inferences about a population parameter. The estimation process is based on probability theory and uses a variety of statistical tools.

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Get a sense of what GSCI 1003 Truth And Lies: Understanding Statistics is all about with this free assessment example. Get a feel for the types of questions you’ll be asked and see how you can best prepare for the class. You’ll also get an idea of what to expect from the professor in terms of workload and difficulty. All in all, this is a great way to get a leg up on your GSCI 1003 Truth And Lies: Understanding Statistics class.

GSCI 1003 Truth And Lies: Understanding Statistics is a class that explores the use of statistics in our everyday lives. The coursework revolves around case studies and real-world examples to help students understand how to properly interpret data. In addition, the class emphasizes the importance of critical thinking when it comes to statistics. As such, students can expect to spend a fair amount of time discussing and debating various statistical concepts.

Assignment Brief 1: Describe and discuss the essential concepts of statistical thinking

The essential concepts of statistical thinking revolve around understanding and making use of probability, which is the mathematical study of uncertainty. Probability helps us to understand how likely it is that an event will occur, and can be used to make informed decisions in a variety of situations.

For example, if you’re considering whether or not to buy a lottery ticket, you might want to know what the chances are that you’ll actually win. Probability can also help us to understand trends in data, and to make predictions about future events. By understanding and using probability, we can become more statistically savvy and make better decisions as a result.

There are a few key concepts that are essential to understanding probability.

First, it’s important to know that probability is a measure of likelihood, not a certainty. This means that even if the probability of an event happening is very low, it’s still possible that it will happen.

Second, the probability is affected by both chance and choice. Chance events are those that are out of our control, like flipping a coin or rolling a dice. Choice events are those that we can influence, like deciding which card to pick from a deck.

Third, probability can be represented using numbers, graphs, and tables. These representations can help us to visualize likelihoods and make comparisons between different events. Finally, it’s important to remember that probability is not the same as prediction. Probability can tell us the likelihood of an event happening, but it can’t always tell us exactly when or where it will happen.

Assignment Brief 2: Organize and present data in appropriate format 

Data can be presented in a variety of formats, depending on the type of data and the purpose of the presentation. Some common formats for presenting data include tables, graphs, maps, and charts. 

Tables are a common way to present data in lists or rows and columns. Tables can be used to compare data, show relationships between data points, or simply to display a large amount of data in an organized way. 

Graphs are another common format for presenting data. Graphs can be used to show trends over time, compare different groups of data, or illustrate relationships between different variables. 

Maps are often used to visualize geographical data. Maps can be used to show where something is located, to compare data across different regions, or to show the distribution of something across a geographical area. 

Charts are another type of graphical representation of data. Charts can be used to compare data, illustrate trends, or show relationships between different variables.  

When choosing a format for presenting data, it is important to consider the type of data being presented and the purpose of the presentation. Some formats are better suited for certain types of data or for certain purposes than others. Choose the format that will best allow you to communicate your data effectively.

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Assignment Brief 3: Differentiate between descriptive and inferential statistics 

Descriptive statistics are used to summarize or describe a set of data. Inferential statistics are used to make inferences about a population from a sample of that population.

For example, let’s say you want to know the average height of men in the United States. You would use descriptive statistics to calculate the mean, median, and mode for the heights of American men. From this information, you would be able to say something like “the average height of American men is around 70 inches.”

However, if you want to know if the average height of all men in the world is different from the average height of American men, you would use inferential statistics. You would take a sample of men from around the world and compare their heights to the heights of American men. If the two groups are significantly different, you could say that the average height of men in the world is different from the average height of American men.

However, if you wanted to know whether there was a difference in the average heights of American men and women, you would use inferential statistics. This is because your sample (the group of American men that you measured) is not representative of the entire population (all American men). You would use a statistical test, such as a t-test, to see if there is a significant difference between the two groups.

Assignment Brief 4: Investigate methods used to describe data 

There are a variety of methods used to describe data, ranging from simple summary statistics to more advanced techniques such as machine learning. Here, we’ll take a brief look at some of the most common methods used.

One of the simplest ways to describe data is by using summary statistics such as mean, median, and mode. This can give you a quick overview of how your data is distributed. For example, if you’re looking at IQ scores, you might find that the average score is 100 with a standard deviation of 15.

Another common method for describing data is through visualizations such as histograms and scatter plots. These can be helpful for spotting patterns and trends in your data. For example, you might notice that IQ scores tend to be normally distributed, with most people scoring between 85 and 115.

More advanced methods for describing data include techniques such as clustering and classification. These can be used to group data points together based on similarities or to predict values for new data points. For example, you could use a clustering algorithm to group people together based on their IQ scores, age, and education level.

Finally, machine learning is a powerful tool for describing data. This can be used to build models that make predictions or recommendations based on past data. For example, you could use a machine-learning algorithm to predict how likely someone is to score high on an IQ test based on their age, education level, and other factors.

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Assignment Brief 5: Demonstrate ways that statistics can be misleading 

There are a few different ways that statistics can be misleading.

One common way is through the use of averages. For example, if you have a population of five people, and two of those people are 20 years old and three of those people are 70 years old, the average age in the population is 40. But if you looked at only the 20-year-olds in the population, then the average age would be 0.

Another way statistics can be misleading is by looking at data from too small a population. For example, if you want to know what percentage of adults in the US have tattoos, but your dataset only includes people who live in San Francisco, your statistic will be different than if you included data from the entire country.

Finally, statistics can also be skewed by outliers. For example, if you’re looking at the average height of people in a population, and one person in that population is 10 feet tall, the average height will be artificially high. If you’re looking at data that has a lot of outliers, it’s often helpful to look at the median instead of the mean, because the median is less likely to be skewed by outliers.

Assignment Brief 6: Interpret the concepts used to analyze data in various fields (e.g., research, quality control, political polling, averages, etc) 

There are a few key concepts that are used to analyze data in various fields. These include research, quality control, political polling, averages, and so on. Each of these concepts is important in understanding how to properly interpret data.

Research is a process of gathering data and information in order to gain insights or answer questions. This can be done through surveys, interviews, observation, or experimentation.

Quality control is a process of ensuring that data meets certain standards. This can involve checking for accuracy, validity, and reliability.

Political polling is a process of surveying people in order to gauge their opinions on various political issues. This can be used to predict election results or understand public opinion on certain policies.

Averages are a way of summarizing data by finding the mean, median, or mode. This can be useful for understanding overall trends or patterns.

Research is important in order to understand what kind of data is being collected and how it can be used. Quality control is important in order to ensure that the data meets certain standards. Political polling is important in order to gauge public opinion. Averages are important in order to get an overall picture of what the data looks like.

Each of these concepts is important in its own way. All of them combined give us a good understanding of how to interpret data properly. By understanding these concepts, we can better understand what the data is telling us and how to use it effectively.

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