>25
2,000,000
7
To create a linear graph, you need at least two data points. These points are necessary to establish a line, as they define the slope and intercept of the linear relationship. However, having more data points can help to better visualize the trend and assess the linearity of the relationship.
6
wifi network
wireless sensor networks
yes
Mostly through statistics, or summaries of the data set (depending on the type of data). There are many different statistical methods used to analyze the many different types of data that come from research studies or experiments. However if you just want a relatively quick and simplistic overview of a set of data than you should follow SOCS: Shape, Outliers, Center, Spread. Shape (the shape of the graphed data points) Outliers (any data points that fall outside the realm of "normal") Center (where the data points are mostly centered around) and Spread (the range of the data points). This should give you some immediate conclusions from your data.
The population is every data point you intend to generalise the survey results to. The sample frame is those data points that you can pick from for the survey. The sample is which of these data points you actually survey, and the sample size is how many of those data points there are. For instance, if you have 700 students in a school, and you have access to 300 of them, and decide to give 30 of them a survey, the sample size is 30.
To find the percentage for a stem-and-leaf plot, first determine the total number of data points represented in the plot. Then, count how many data points fall into the category or range of interest. Finally, divide the count of the specific category by the total number of data points and multiply by 100 to convert it into a percentage.
A histogram is the type of graph that uses the length of bars to represent the frequency of data points within specified intervals, known as bins. Each bar's height corresponds to the number of data points that fall within that range, making it easy to visualize the distribution of the data. Histograms are commonly used in statistics to illustrate the underlying frequency distribution of a set of continuous data.
Using too many control points when georeferencing can lead to overfitting of the transformation model, causing distortion in the georeferenced data. This can result in inaccurate spatial positioning of the data and reduced overall accuracy of the georeferencing process. It is important to use an appropriate number of control points to balance accuracy with computational efficiency.