10th-Grade-Math---USA
Linear-Functions
Easy
Question
The 12th term in sequence -8, -5.5, -3, -0.5, 2.0 …. is
- 19.5
- 12.5
- 18.5
- 17
The correct answer is: 19.5
Related Questions to study
10th-Grade-Math---USA
The value of f(0.5) if
is
The value of f(0.5) if
is
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
The linear regression equation is y = 4.7x + 51. The scatter plot at x = 5 is (5, 90). The residual associated with the point where x = 5 is
The linear regression equation is y = 4.7x + 51. The scatter plot at x = 5 is (5, 90). The residual associated with the point where x = 5 is
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
The value of y when x is taken as 9 with the help of following table is
x |
4 |
5 |
6 |
7 |
8 |
10 |
12 |
y |
1.2 |
1.5 |
1.8 |
2.1 |
2.4 |
3.0 |
3.6 |
The value of y when x is taken as 9 with the help of following table is
x |
4 |
5 |
6 |
7 |
8 |
10 |
12 |
y |
1.2 |
1.5 |
1.8 |
2.1 |
2.4 |
3.0 |
3.6 |
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
The equation that could represent a possible trend line for the data in the scatter plot is
![](data:image/png;base64,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)
The equation that could represent a possible trend line for the data in the scatter plot is
![](data:image/png;base64,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)
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
The true statement among the following regarding the data in the table is
x |
3 |
4 |
7 |
8 |
10 |
y |
1 |
9 |
2 |
8 |
3 |
The true statement among the following regarding the data in the table is
x |
3 |
4 |
7 |
8 |
10 |
y |
1 |
9 |
2 |
8 |
3 |
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
Identify the range of following relation
R = {(a, 1), (a, 2), (b, 2), (c, 3)}
Identify the range of following relation
R = {(a, 1), (a, 2), (b, 2), (c, 3)}
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
If the explicit formula for an arithmetic sequence is
, then the recursive formula for the sequence is
If the explicit formula for an arithmetic sequence is
, then the recursive formula for the sequence is
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
If sequence has 98 as first and -2 as the decrement, then the term in the sequence being 0 is
If sequence has 98 as first and -2 as the decrement, then the term in the sequence being 0 is
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
If 7th term of sequence is 11 and 11th term of sequence is 7, then the common difference of the sequence is
If 7th term of sequence is 11 and 11th term of sequence is 7, then the common difference of the sequence is
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
The 19th term of sequence 8, 5, 2, -1, …. is
The 19th term of sequence 8, 5, 2, -1, …. is
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
A linear function of slope ½ is multiplied by 4. The slope of new resultant function is
A linear function of slope ½ is multiplied by 4. The slope of new resultant function is
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
The owner of Horizon flight school created a scatter plot and calculated line of best fit for her enrollment data as shown in the table. The equation of the line of best fit is y = 1.44x + 877. The residuals for 6th year is
![](data:image/png;base64,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)
The owner of Horizon flight school created a scatter plot and calculated line of best fit for her enrollment data as shown in the table. The equation of the line of best fit is y = 1.44x + 877. The residuals for 6th year is
![](data:image/png;base64,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)
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
The table shows the number of customers y at a store for x weeks after the store’s grand opening. The equation for the line of best fit is y = 7.77x + 38.8. Assuming the trend continues, the reasonable prediction of the number of visitors to the store 7 weeks after its opening are
![](data:image/png;base64,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)
The table shows the number of customers y at a store for x weeks after the store’s grand opening. The equation for the line of best fit is y = 7.77x + 38.8. Assuming the trend continues, the reasonable prediction of the number of visitors to the store 7 weeks after its opening are
![](data:image/png;base64,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)
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
The true statement regarding r = 0.1 is
The true statement regarding r = 0.1 is
10th-Grade-Math---USALinear-Functions
10th-Grade-Math---USA
Identify the association from the data in the table
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAVsAAABJCAYAAACTpoXJAAAI/ElEQVR4Ae1cS27rOBDUgXIaA9nPdm4RBL6Kc4nsk4NkkWNwQH2pkjSmY3XJ3a4AgdWyYnZ9VKToh9eklNLn52d+0Y8YEANiQAwYMdDkz1XYGrGrjxUDYkAM9AwobGUFMSAGxACBgTFsX15ekn7FgTwgD8gD+3gA83sWtvimx/rt7c1j29U9C181VQ95YXT9tkh/Ntx5wsIfhS0y8uB1dNMK34Mb8I/tRdcVaVHYIiMO6+imFT6HpqxoObquSIHCFhlxWEc3rfA5NGVFy9F1RQoUtsjIHvXPJZ2aJjXvX3t82tXPYJr29+OUmoyt/T0nBkIGvq/3ARO8vl7S71UF7ruAgQ87nOt4SpcfvMK+5uD+TZfXJp2/V/AM92nv59OHrdIK2xUN7jv1lc7NKZ1eo4VtZ1rWBFJqwLkpyxG74xzA1jdgHomNrwvaYqL8Pqem4QeuLe7er32QLsM236dlCGO99MO9Z/hh2wrbrx6GlV85wwzn7kVW/L2tqMVAaRKsXSkZYClHG44p+LJuhFXegKl8peArB8zHrU+LQML3d6y5+LoQmk8ia+d2BLjxURzc0z1ZttFOOOBn63uWH7YZ8Ri43Ww6PsIZhdMRoloLVxqHgS/jOb2WWwjRVu4lo9zwYeg3oVvHxvTr0AsH9/+ELeTNWgAPve7xekzYppTme0aN6arJXtSloEzz2uPrbtD5FkKHeX5uD0suP8MeH4xJXNXmkdn4Wm+SV3XAcFtycC/vzTz42v0ZNmxHwO2eiu1+kbmo5TZIv0fE/BLJHF9aXw1Zm3O4Qe3xDSO1roS9vPI9m2MuvoyhnzzBq/OtBRus5adycK+HbetdrWxLOfY55og673Vt5pxfsV/FwLeGJ2LYsjCV6jP0K8dbHq8H0vK6fc9wcK9jW9N5zeN7Ij5mG2Hcs+2+gGhB5lkWZpq9gHJEnXdrLVw5GgVfv3qfvtXtTMxYDVHwtYSu35gl1xbHPHxr3a9tEa1dt/85Du4tTfE81vvj5YftGLTFP6spH8MNApcj6lyccGGb4RXa5W0SRtDmYVn6tZrBXuZcVZuKhW/qvguWYauLpeM0fndki3t9q2RaLKSUytwh+Jkftsg4obYVlQDgyhDCd4WgB387un5b9D8bboXtlhMcnY9uWuFzZMYbWo2uK1KhsEVGHNbRTSt8Dk1Z0XJ0XZEChS0y4rCOblrhc2jKipaj64oUKGyREYd1dNMKn0NTVrQcXVekQGGLjDiso5tW+ByasqLl6LoiBQpbZMRhHd20wufQlBUtR9cVKVDYIiMO6+imFT6HpqxoObquSIHCFhlxWEc3rfA5NGVFy9F1RQoUtsiIwzq6aYXPoSkrWo6uK1KgsEVGHNbRTSt8Dk1Z0XJ0XZEChS0y4rCOblrhc2jKipaj64oUKGyREYd1dNMKn0NTVrQcXVekQGGLjDiso5tW+ByasqLl6LoiBQpbZMRhHd20wufQlBUtR9cVKVDYIiMO6+imFT6HpqxoObquSIHCFhlxWEc3rfA5NGVFy9F1RQoUtsiIwzq6aYXPoSkrWo6uK1KgsEVGHNbRTSt8Dk1Z0XJ0XZGCq2GbCdGvOJAH5AF54D4PXA1bTGePdTZJ5B/h861udP221Hk23ArbLSc4Oh/dtMLnyIw3tBpdV6RCYYuMOKyjm1b4HJqyouXouiIFCltkxGEd3bTC59CUFS1H1xUpUNgiI3fUvx+n1DRN/3tKl587PuyGP2WY9ut9wAWvr5f0e0Ovf7mUgS+lr3QetSswvn/9peWb/oaDr2jp55JOgPX8XbxPOqTifgDMCtudjNUF7TmNt+b3OTUNJ3Cppi34ygF8+rCO2tT+a5hiWKPDLmzDh05m7/s8160NIo5XS/Govn0AzPSwnVZ/RTCl33R57VcTBisJe1G7/ufBs3autNp+x/b4VnptJ5NSw5VrdjrFwfdEYbvQ5RjsHF0XYPsTfMz0sC0f16ZVRAfcaiVoL+p6sLaP3gaTB9rHHh+OuI4Xr9qr5uAbPDhtIcwnz73QLD+Hg2857niG+BQ2jplYTyzliMXxAZgPCNuUxtXtEEQt8CY1Rvt/DDO3wQr9hw1b4qo23x4M/YrbsDvs9/imBcHiit1OHIIvFfdh0yQGTiTsCNxj9hyA+ZCwTeNmdfcY2oaSIXiOqN1qb/qCrFshMVZHHHzDrcJ//OLiG3CmFHaynCD2R3xN88BH6dqB5mM+JmxTb+Qmf8Fy6b8Fttv/O0ZUnphMfO3KAFbwi3t35xNMfGXrOWzjTZYlwumYhXUa8eiw7TKIoe+A+bCwzd+IlqtAS9D8m7Vf5Q7bJAPbRq88fLwJpKSKgS9PIrNHaeKeHgNfyWcO1hlW4pZJ2QcT9yNgzmF7+uffkoLU5Orz8zOtJfHsyruK8rHb9p+dcETtgmiYQCwnD6Sdg69/GiGvajNWCj6Y/K2+rEXtaPjKgRdYIXzLaw2PKboO/T8A5gPDdtpKaIxXgFRRB3GJr8JHJNtgqOj6bVH2bLiPC9vxSzL7WTW6qMK3dTv7OB9dvy0Vng33YWGb91DaR27CY2l0UYVv63b2cT66flsqPBvuw8J2SwCL89FFFT4L1/A+M7p+W0w+G26F7ZYTHJ2Pblrhc2TGG1qNritSobBFRhzW0U0rfA5NWdFydF2RAoUtMuKwjm5a4XNoyoqWo+uKFChskRGHdXTTCp9DU1a0HF1XpEBhi4w4rKObVvgcmrKi5ei6IgUKW2TEYR3dtMLn0JQVLUfXFSlQ2CIjDuvophU+h6asaDm6rkiBwhYZcVhHN63wOTRlRcvRdUUKFLbIiMM6ummFz6EpK1qOritSoLBFRhzW0U0rfA5NWdFydF2RAoUtMuKwjm5a4XNoyoqWo+uKFChskRGHdXTTCp9DU1a0HF1XpCCHLf6Q/vNwHNauji6q8Nl5h/HJ0fXb4vDZcCtst5zg6Hx00wqfIzPe0Gp0XZEKhS0y4rCOblrhc2jKipaj64oUXA3bfIF+xYE8IA/IA/d7AAN43LPFN1SLATEgBsTAfgwobPfjUp8kBsSAGNhkQGG7SY3eEANiQAzsx8B/Pzv/Zckj7f0AAAAASUVORK5CYII=)
Identify the association from the data in the table
![](data:image/png;base64,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)
10th-Grade-Math---USALinear-Functions