For 1.
Positive correlation means if you plot a country's population and land size on a graph. There would be a linear regression line that's sloping upwards.
Since it's a scatterplot, there will be some that fall out of the line, but most of them should be on the line.
It's (A)
For 2.
An outlier is a datapoint that's far, FAR away from the others, so it's (B)
For 3. (A), it's a single line-of-best fit that runs through the middle of the cluster of data points.
Answer:
The problem domain refers to the support options.
Hi,
I changed your program using some of the concepts you were trying to use. Hopefully you can see how it works:
#include <string>
#include <iostream>
#include <sstream>
#include <vector>
#include <algorithm>
using namespace std;
int main()
{
short T;
cin >> T;
cin.ignore();
string str[100];
for(int i=0; i<T; i++)
{
getline(cin, str[i]);
}
for (int i = 0; i < T; i++)
{
stringstream ss(str[i]);
string tmp;
vector<string> v;
while (ss >> tmp)
{
// Let's capitalize it before storing in the vector
if (!tmp.empty())
{
transform(begin(tmp), end(tmp), std::begin(tmp), ::tolower);
tmp[0] = toupper(tmp[0]);
}
v.push_back(tmp);
}
if (v.size() == 1)
{
cout << v[0] << endl;
}
else if (v.size() == 2)
{
cout << v[0][0] << ". " << v[1] << endl;
}
else
{
cout << v[0][0] << ". " << v[1][0] << ". " << v[2] << endl;
}
}
return 0;
}
Answer:
see below
Explanation:
The program of interest is the function "findMode[x, n]" in the attached. It is written the Wolfram Language of Mathematica.
The basic idea is that the data in the array is sorted. The sorted array is partitioned into sets of identical elements, and the number in each of those sets is counted. The maximum of those counts is the mode. The location of the maximum count corresponds to the location of the set having that count. We use that location information to pull out the mode value(s).
If there is more than one mode, all are reported.
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An example data array is provided, along with the program output.
