![]() ![]() Before we collect any data, I ask the students to write down their own hypothesis at the top of the A4 graph paper. The line of best fit passes through each of the points.Ĭreating Scatter Graphs from Primary DataĪs an extended plenary I challenge the students to create a scatter graph based on their own hand and foot size. In my experience, there are three main misconceptions when drawing lines of best fit. It is noted in several examiners reports by AQA and Edexcel that students are more likely to correctly estimate the value of a missing data point and identify anomalous data points if they use a line of best fit. The line of best fit also helps to predict the value of one variable when the other is known. We discuss the strength of the correlation as an indication of how closely two variables are related. The closer the points are to the line of best fit the stronger the correlation. Line of Best FitĪ line of best fit can be used to clearly illustrate the directional trend of the data. If two variables are not related the points will be scattered so no correlation is apparent. A negative correlation means as one variable increases the other will decrease.A positive correlation means as one variable increases, or decreases, so does the other.When we have plotted the points, I introduce the term correlation as a means to describe the relationship between two variables. In later examples on plotting scatter graphs and understanding correlation I expect students to choose and draw their own axes on A4 graph paper with appropriate scaling. I ask the class to sketch on their mini-whiteboards what the scatter graph might look like if our hypothesis is correct.įor the first couple of examples I provide the scaled axes for the class. The consensus is the more time people spend reading the less time they are likely to spend watching TV. The areas have been divided into four geographic regions: 1=North- East, 2=North-Central, 3=South, 4=West.Plotting Scatter Graphs and Understanding CorrelationĪs we begin the first example we discuss the type of relationship we expect to see when time spent reading is plotted against time spent watching TV for a sample of ten people. The data set provides information on ten variables for each area from 1976 to 1977. It contains data from 99 standard metropolitan areas in the US. Go through the dataset and try to understand what the columns represent.Next, we'll be looking at a pre-recorded session on Data.The temperature on Mars and the stock market have an almost zero correlation because the stock market price will not depend on the temperature on Mars.It was raining this morning, and the grocery store was out of bananas.There is no relationship between the amount of tea drunk and the level of intelligence.It means that when the value of one variable increases, the value of the other variable(s) also increases (also decreases when the other decreases). Two features (variables) can be positively correlated with each other. It is recommended to perform correlation analysis before and after a data science project's data gathering and transformation phases. However, more often than not, we oversee how crucial correlation analysis is. Importance of CorrelationĮvery successful data science project revolves around finding accurate correlations between the input and target variables. Target variable - In data science, The "target variable" is the variable whose values are to be modeled and predicted by other variables in the dataset. Variable is often interchangeably used as features too. Now you may ask, what is a variable? - If we go back to the scatter plot example: temperature and ice-cream sales are variables. It measures the strength of a linear relationship between two quantitative variables. ![]()
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