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Im doing a science project for school. The question is Can people taste the difference between regular and reduced fat foods?
I changed the type of foods Oreos and reduced fat oreos, nillas and reduced fat nillas, cheese bits and reduced fat cheese bits
there was 5 people and all of them could taste the difference
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The y-axis on the graph below represents the values taken by the?
By the y variable - whatever that may be. It may be a dependent variable or another independent variable.
What is the difference between the controlled variable and an independent variable?
A controlled variable is one which you try to keep constant. The independent variable is the on that you change. The dependent variable is the one you measure. For instance, how much will a drink cool down in one minute when you add a certain number of ice cubes to it? The independent variable is the number of ice cubes. The dependent variable is the temperature drop. There are many possible controlled variables: the starting temp of the drink, the size of the ice cubes, the type of glass, the air temperature, and so on. See related link below.
True or false The independent variable is the variable that is changed in an experiment?
false
What is the difference between corelation and regression?
I've included links to both these terms. Definitions from these links are given below. Correlation and regression are frequently misunderstood terms. Correlation suggests or indicates that a linear relationship may exist between two random variables, but does not indicate whether X causes Yor Y causes X. In regression, we make the assumption that X as the independent variable can be related to Y, the dependent variable and that an equation of this relationship is useful. Definitions from Wikipedia: In probability theory and statistics, correlation (often measured as a correlation coefficient) indicates the strength and direction of a linear relationship between two random variables. In statistics, regression analysis refers to techniques for the modeling and analysis of numerical data consisting of values of a dependent variable (also called a response variable) and of one or more independent variables (also known as explanatory variables or predictors). The dependent variable in the regression equation is modeled as a function of the independent variables, corresponding parameters ("constants"), and an error term. The error term is treated as a random variable. It represents unexplained variation in the dependent variable. The parameters are estimated so as to give a "best fit" of the data. Most commonly the best fit is evaluated by using the least squares method, but other criteria have also been used.
How often the value of a random variable is at or below a certain value?
How often the value of a random variable is at or below a certain value.
